But here is the good news: you don’t need to be fluent from day one.

Google Ads is a massive ocean, and even the pros are constantly learning new “dialects.” The secret isn’t knowing everything immediately; it’s about rolling up your sleeves and starting with the essentials.

In this guide, we’re breaking down the two heavyweights of e-commerce: Standard Shopping and Performance Max (PMax). We’ll look at what they actually do and, more importantly, which one deserves your budget.

What Are Shopping Campaigns?

Think of Shopping campaigns as your digital shop window. When someone searches for a specific product on Google, these ads show them exactly what they’re looking for: a clear photo, the price, and your shop’s name.

These campaigns live and breathe through your Product Feed (the list of products you send from your website to Google Merchant Center).

You’re the Teacher. Standard Shopping gives you the control. It’s like teaching a student exactly what to do:

• Search Term Transparency: You can see exactly what words people typed before clicking your ad.
• Manual Control: If a specific product isn’t selling, you can lower the bid or stop showing it entirely.
• Granular Insights: You get to see the “why” behind your spend.

What Is Performance Max (PMax)?

Performance Max is Google’s “all-in-one” automated powerhouse. Instead of just sticking to search results, PMax puts your ads everywhere: YouTube, Gmail, Search, Display, and even the Discover feed.

You provide the headlines, images, videos, and Google’s AI decides where and when to show them.

You’re the Manager. Unlike Shopping, you don’t pick the keywords. You’re essentially hiring an expert and saying, “Here are the tools and the goal, go and find me customers wherever they are.”

The Quick Comparison

Which One Should You Choose?

Go with Standard Shopping if:

• You want full control over optimisation
• You want to know exactly which search terms are costing you money.
• You’re in a niche where you need to be very specific about where you appear.

Go for Performance Max if:

• You want to find customers across different platforms (like YouTube).
• You have high-quality photos and videos ready to go.
• You trust the algorithm to find conversions you might have missed manually.

Many advertisers actually use both together.

Tip: If you run both at the same time for the same products, PMax will usually take priority. It’s often better to start with Shopping to learn the ropes before moving to PMax to scale.

How to Set Budgets

There’s no one-size-fits-all, but here’s a simple starting point:

1. Start small: Start with a low daily spend. Treat this initial phase as a test where the primary goal is gathering data, not immediate profit.
2. The 3-Week Freeze: Once you launch, do not modify your campaign for at least three weeks. The system requires this specific window of time to analyze user behavior and stabilize performance.
3. Avoid Frequent Changes: Resist the urge to make daily adjustments. Constant manual updates prevent the platform from optimizing correctly and will likely reset the learning phase.

In Summary

Google Ads doesn’t have to be a nightmare. It’s just a new language, and like any language, you’ll get better with practice. Start small, keep an eye on your ROAS (Return on Ad Spend), and don’t be afraid to test.

The post <strong>Shopping vs Performance Max: A Beginner-Friendly Guide</strong> appeared first on GROWTH ART.

With the rapid growth of online marketing, the term performance marketing has also gained importance. In performance marketing, the interactions with the targeted target group are converted into KPIs, which are used to align the campaign’s activities. These can be measurable numbers such as the click rate, the conversion rate, or the impressions. The goal is always maximum efficiency. With the help of clicks, leads, or completed sales, the success of a measure is determined and the costs of the individual actions can be scaled. The basic elements in performance marketing are measurability and optimization to increase efficiency.

• Measurability: The interactions with the user are recorded quantitatively and converted into KPIs. These form the basis for ongoing optimization of the measures.
• Optimization: Every single online activity that takes place as part of a campaign can be measured separately and compared with others. Optimization approaches can be derived from the data obtained. Even in existing campaigns, larger or smaller adjustments can be made on an ongoing basis to guarantee overall success.

Which Areas Are There In Performance Marketing?

Four different areas build on each other in terms of time and logic. You start with the goal setting, then select the appropriate tools that will be measured and optimized over the course of the campaign. At the beginning of every marketing campaign, there is goal-setting. In the case of performance marketing, the goals must be easily measurable. You can orientate yourself on different aspects when setting goals:

• Marketing goals like increasing awareness
• Corporate goals such as maintaining market share
• Goals with a focus on the customer such as strengthening loyalty
• Campaign-relevant goals such as increasing the number of visitors

The second step is to find appropriate measures that will best achieve the objective. You can choose from various online marketing instruments:

1. Search Engine Advertising: Paid advertisements are placed under the keyword SEA (Search Engine Advertising). The aim is to improve the ranking.
2. Search Engine Optimization: Measures within the framework of SEO (Search Engine Optimization) also aim for a better ranking. Thereby one wants to be found optimally in the organic results of the search engine. Functioning SEO measures are, for example, keywords, indexed pages, or backlinks.
3. Display Advertising: This is understood to be graphic ads such as banners that appear on target group-relevant websites. For example, the impressions or the click-through rate are counted.
4. Social Media Marketing: This means all marketing activities on social media platforms from building your community to paid ads.
5. E-mail Marketing: The sending of e-mails can also be integrated into a performance marketing campaign in a variety of ways. Possibilities are, for example, offers, feedback, or reminders by email. Sending a newsletter also falls into this category.
6. Affiliate Marketing: For some campaigns, the placement of offers on relevant websites is suitable. As part of affiliate marketing, you only pay for it when the link is clicked.

If the measures taken are running, the success numbers are measured for each instrument. This makes the effect visible and can be analyzed. Tools like Google Analytics help and allow a detailed analysis of the measured results. The most important metrics include clicks, traffic, conversion.

Performance Advertising and Billing

Advertising in the context of performance marketing also follows the performance principle. The billing depends on the success of the paid advertising measure. Various billing models are available for this:

• CPC: Cost per Click: A certain amount is due for each click.
• CPA: Cost per Action: Pay for a specific action.
• CPL: Cost per Lead: Costs are only due if a user leaves their contact details.
• CPS: Cost per Sale: Payment is only made if the sale is successful.

Performance marketing alone is not a guarantee of success for your company. The more colorful the bouquet of marketing measures, the more successful the entire arrangement. As part of performance marketing itself, the achievement of the set goals depends on competence in analyzing and evaluating the data collected. So deal with the matter in-depth so that you can benefit optimally from performance marketing. Get in touch if you need support for your online marketing strategy.

5 Steps In Performance Marketing

One of the most important goals for any marketer is undoubtedly to create successful and functioning marketing campaigns. But how exactly can this be achieved? Marketing campaigns are becoming more and more complex. There are many variables and details to consider, but also great complexity in almost all of the processes involved. In this article, we’re going to introduce you to important approaches you can take to improve the performance of your digital marketing campaigns.

1. Set measurable goals
2. Select channels and allocate budgets
3. Define KPIs
4. Constantly optimize the campaign
5. Permanent monitoring / controlling

Expertise in handling the analysis tools is important for the success of performance marketing. You should also be well versed in evaluating the key figures determined. Because that is how the success of your optimization measures stands and falls. If you analyze and evaluate competently, then the required effort will be offset by the continuous perfecting of the measures.

We would like to give you a few ideas on what to consider when planning your digital marketing campaigns. Of course, other factors determine success or failure. However, the following points should provide an important first overview of how you can improve the performance of your digital marketing campaign. Read these through before you start planning your next marketing campaign.

• Dozens of templates for data-driven marketers
• Expert tips on how to improve your marketing
• Checklists and how-to guides for campaign optimization
• Best tools for AI in marketing
• Recorded webinars

We have listed many approaches to improve the performance of your digital marketing campaign by planning, implementing, and considering these points before starting your marketing campaign. As you can see it is all interrelated and for your paid campaign to be successful there are many other things to consider before creating your ad and clicking “Start My Campaign”. Nonetheless, in the modern world where Artificial Intelligence (AI) has already been proven to provide help and has proven to be a more efficient and often more successful option for marketing, the same can be said for the performance of marketing campaigns.

The post What is Performance Marketing? appeared first on GROWTH ART.

Being able to predict market trends and customer preferences is the dream of all business leaders. Data analytics applications have what it takes to make this dream come true. But be careful: the right technology is only one side of the coin. Up to now, capital, labor, and raw materials were the classic production factors that have now received a new and serious competitor: data. A lot of data. According to various estimates, the global volume of data is currently doubling approximately every two years and is becoming more and more important.

 But where does all this data come from? The answer to this is very simple: From anywhere and everywhere includes, for example, social media channels, mobile phones (smartphones), search engines, financial transactions, RFID chips (radio-frequency identification), GPS data (global positioning system), and a wide variety of merchandise management systems and almost any other digital application or device. The subject of cloud computing also plays an important role in the collection of data.

The greatest challenge, however, lies in the art of generating structured data from unstructured data in order to use it economically or scientifically. Thanks to big data analytics, we now have the option of interpreting the data quickly and precisely in order to be able to make relevant business decisions on this basis. Companies that have developed this big data core competence for themselves enjoy a strategic advantage. Because they are able to predict information such as market trends and customer preferences. Systems that support them in this process must above all meet two requirements: The quality in relation to the processing of data must be right, and they must be able to prepare data in a meaningful way so that it is useful for decision-makers.

Implementation of Big Data Analytics Project In 5 Essential Steps

It is an illusion to believe that buying an expensive suite of software that does everything at your fingertips. Therefore, you should first all have the patience and time to structure and implement your big data project. Good planning is the most important success factor when implementing such a data analysis system. Before starting a project, it is advisable to take the following five measures.

1. Identify the problem: Before you invest time and money, first find out what problems your company is facing and how they would have to be solved. Are you looking for predictive trends in your market, are you looking for new customers or sales markets, or do you want to optimize your supply chain? Without a precise and structured plan, it could end up taking more time and money than is actually necessary and, in the worst case, will not lead to the hoped-for result.
2. Define success factors: Take sufficient time in advance to identify those key figures that are required for the progress of the project and the expected results.
3. Identify key data: Before pursuing a particular software or solution, make sure that you have full access to the relevant data and that you have a sufficient amount of data (information). No project without data.
4. Assemble talent: do you have the right employees? A suitable team has the appropriate know-how to be completing the project before, during, and after implementation to be able to ride. If this is not the case, your project is doomed from the start. Make sure in good time that you have the right people in-house or get external support.
5. Testing, testing, testing: Before using your big data development in live operation, make sure that you have tested your system sufficiently and the information generated correctly. After all, you want to give the decision-makers the right information and not lead your company down the wrong path. Finally, an important note: Remember that a big data system is constantly evolving and such a project will never be completed. New problems arise, the business strategies of the decision-makers change, new markets are added or customer behavior changes over the years. Regular adjustments and controls as well as the further development of the big data project are essential even after the system has been commissioned. Comprehensive tests include the following test criteria:
6. Functional tests or functional tests
7. Non-functional tests
8. Interface tests
9. Failure tests
10. Data consistency tests
11. Restart tests
12. Interoperability tests
13. Installation tests
14. Surface tests
15. Stress tests
16. Crash tests
17. Load tests
18. Performance tests
19. Computer network tests
20. Security tests

The test itself can be carried out according to various methodological approaches and is based on the test strategy (SMART testing, risk-based testing, data-driven testing, exploratory testing, top-down/bottom-up, hardest first, big-bang).

Implementation and Operation

The first step in the practical implementation of the big data strategy is the implementation of the selected platform according to the plan from the architecture phase. We take on the installation and configuration of the system either independently or, better, in cooperation with the customer’s team, which then takes responsibility. In addition to the installation of the big data platform itself, this also includes the connection of the various data sources and the normalization of the data for successful evaluation and correlation. In some cases, we also develop new approaches for integrating unusual data sources, or we implement additional software components for any data pre-processing that may be required.

In this phase, we also develop the required roles and rights concept with the customer. It is often difficult to decide in advance “at the green table” which roles are required, which user groups should access which data, and who should perform which functions. During the implementation and connection of the data, it often becomes clear very quickly how granular the access controls should be set up in order to meet all requirements. For us, a central component of this phase: the documentation of the customer-specific solution and the data sources that have been set up. Even a small big data environment quickly turns into a complex structure, precisely because a wide variety of data from distributed systems often converge in it. Consistently maintained documentation (e.g. in a wiki) makes later work with the new system considerably easier.

Even after the first phase of implementation, the big data solution needs ongoing care and maintenance. Often new data sources are added, the existing data sources change or there are interruptions in the delivery of data due to problems in the IT infrastructure. In order to ensure the desired availability of the data in these cases as well, the operation of the big data platform must be planned and implemented just as professionally as for any other IT system. On request, we also support our customers during operation and take over all necessary tasks, including the necessary updates and extensions.

The post Implementation of Big Data Analytics Project In 5 Essential Steps appeared first on GROWTH ART.

We as Growth Art offer you innovative e-commerce platforms based on Shopify or WordPress+WooCommerce and develop solutions for your comprehensive service portal. Since every shop system has different strengths, we support you in finding the right solution for you and your target group and implementing it.

1. Shopify: Shopify is an all-in-one eCommerce solution that makes it easy for you to create an online store, accept payments, and manage your inventory, all from a single platform. With Shopify, you don’t need to worry about the technical aspects of managing an eCommerce site such as web hosting, security, caching, etc.
   
2. WordPress + WooCommerce: WooCommerce is an open-source eCommerce plugin built for WordPress. It allows you to leverage the most powerful content management system (CMS) and use it to run an online store.
   WordPress is the most popular website builder on the planet powering 43% of all websites, and what many don’t know WooCommerce is the most popular eCommerce platform.
   Because of the open-source nature of WooCommerce, you can customize every aspect of your store and easily add custom extensions.

The decision to choose between the two platforms depends entirely on your needs and skill level.

Innovative Solutions From Growth Art Limited

Together with you, our experts will develop the right concept for your connected commerce strategy and provide you with an e-commerce solution tailored to your needs. For this, we offer you various services, such as advice on an optimal e-commerce architecture, the recording of requirements, the conception and design of interfaces, the technical implementation and implementation of the new software solution or individual shop development as well as further services and digital marketing. In addition, our experienced architects will support you in integrating the interfaces of all adjacent systems, such as ERP, CRM, and PIM, into your new system landscape.

As digital and e-commerce consultants, we support our customers across all areas of competence from the development of new business models and strategies for digital growth through the conception and planning of e-commerce projects to their operational support during implementation. Business, technology, organization, and marketing are equally important as consulting fields. We provide the necessary structure, impart specialist knowledge and help you with your e-commerce project with our expertise. Digital trade is growing steadily and making life difficult for conventional business models. In B2C as in B2B, new online providers take over the market shares of wholesalers and retailers without regard. Digitization of sales means: moving with the times, surviving in the market, and overtaking the competition.

How We Specifically Help As E-Commerce Consultants

What exactly does developing an e-commerce strategy mean? What is meant by the digitization of sales? And above all, how can you benefit from the digitization of sales in your industry, with your products, and in your company? With a variety of services, we enable the digitization of sales in your company or take it one step further:

• Adaptation of the business model to e-commerce
• Identification of unique selling points
• Market and competition analysis
• Selection of service providers
• Optimizing processes
• Identification of the necessary applications
• Software and service selection
• Identification and prioritization of requirements
• Analysis of existing concepts and systems
• Development of lead management strategies
• Increase in visibility and traffic
• On-site optimization of the conversion rate
• Analysis of the customer journey
• Building teams within your organization

This is an excerpt from our e-commerce consulting services, for further details please visit our e-commerce consulting page. We are convinced that the future of many companies lies in the digitization of sales. Digitization offers enormous opportunities and potential. As e-commerce consultants, we support you in developing the strategy and designing your project. As coaches, mentors, and trainers, we help you to build up the necessary know-how for a successful e-business. Because we want to create the right basis so that successful companies can survive in the market for decades to come.

In contrast to classic e-commerce agencies, our focus, in addition to professional project support, is always on building up e-commerce know-how in your company. Regardless of whether it is a start in online trading, the introduction of a B2B e-commerce platform, or the establishment of a customer service portal – failure is inevitable without a valid e-commerce strategy that takes into account the individual challenges of your company. If you enable your commerce model to develop, we will take the time to carry out a detailed analysis of your potential and the framework conditions. Our consulting evaluates your marketing and sales processes, considers the organizational and structural starting position, and analyzes your situation in the market as well as the needs of the industry and target group.

If you know and understand the requirements of your customers, the only thing left to do is to solve them as smartly as possible. With a smart strategy, you can start step by step into e-commerce, keep your expenses under control and make your company fit for the future. We will help you with this task so that you can achieve quick results even with limited resources. In the long term, however, companies can only benefit from e-commerce if they themselves have the necessary knowledge. Knowledge transfer, therefore, plays a decisive role in our projects. In parallel to our advisory role, we will build up in-depth e-commerce know-how in your company. In this way, you can do without external help in the future. Get an idea of our individual consulting services for an effective e-commerce strategy.

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Key Figure-Based Online Shop Optimisation

Customer demands, technologies, and markets are constantly changing and there is never a standstill. The closest competitor is always just a click away. For these reasons, continuous optimisation is a must. But where do you start? Our analyzes provide important clues:

• Technical analysis
• Web analytics evaluation
• Heuristic evaluation
• User analysis

By analyzing this data, we gain valuable pointers for improving the entire e-commerce value chain. The performance of an online shop can often be significantly increased within a very short time with small measures. Quick wins start where the greatest weaknesses exist. Based on the analysis results, we develop a tailor-made quick-win concept for you for the short-term improvement of the weakest parts of your e-commerce value chain.

Due to the immediate effect of the measures, we increase traffic, conversion rate, shopping cart size, or sales within a very short time depending on the initial situation and potential.

1. Usability Optimisation: Can the online shop be used? How is the user ergonomics? Are there any barriers preventing visitors from buying? We optimise the usability of your online shop.
2. Conversion Optimisation: Why do so few visitors buy? Thousands of online shop operators are concerned with this question. We optimise your webshop for more sales and better conversion rates.
3. Web Analysis: Where do your visitors leave? Which areas of the shop are particularly important? The web analysis provides us with these and many other important findings.

Individual E-Commerce Strategy

We advise you on the planning and optimisation of your strategic direction and check your current concepts for effectiveness, efficiency, and sustainability.

• Sustainable strategies for e-commerce and digital change
• Market analysis, competition analysis, and internationalization
• Multi-platform and brand strategies
• Tenders and selection of service providers

In addition to extensive business analysis, we design the ideal marketing activities and the optimal online shop for you. In addition, by selecting suitable service providers, we ensure that your internal and external business processes run smoothly.

What We Offer As Growth Art Limited

E-commerce strategies: In a good e-commerce strategy, all components interlock optimally – this is the only way to achieve top performance. We accompany you step by step to your tailor-made strategy.

Digital business models: When planning a new business, nothing should be left to chance. We take over the business model engineering and plan your business idea right from the start – down to the last detail.

Internationalization in e-commerce: Whether legal framework conditions, different user expectations, or local partners. We help you with our extensive expert knowledge to avoid stumbling blocks and develop internationalization strategies that can be implemented for you.

Online marketing strategies: Get fit for your online marketing. Your campaigns will be a success with individual strategies tailored to your company. We accompany you step by step on the way to a tailor-made online marketing strategy. Learn more

Marketplace strategies. Online trading is increasingly being dominated by platform providers. Retailers can hardly get past Amazon or Etsy. But we offer a reliable consultation service about growing platforms such as Amazon and Etsy with Shopify and WordPress. So that you can assert yourself in this unequal competition, we develop strategies for dealing with marketplaces and platforms.

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More and more companies are increasing their sales by offering customers continuous added value, lowering costs, and optimizing operational processes. But how can you take the right actions at the right time? Data analysis helps your company to gain a significant competitive advantage and to plan strategies in advance. With data analysis, you gain insights from data. Today you can analyze data to make real-time decisions. It is also possible for you to recognize new trends and gain insights that would not have been possible with conventional data processes.

A Case Study For Applied Data Analysis

In recent industry surveys, respondents from five countries voted on the top uses for data analytics. The following aspects were identified as relevant:

• Increase in process and cost efficiency (60 percent)
• Promotion of strategy and change (57 percent)
• Monitor and improve financial performance (52 percent)

The respondents also stated that these factors are supported by cloud computing, big data, and artificial intelligence or machine learning. Cloud computing, in particular, helps to extract data quickly and efficiently. In this way, a company can keep up with a culture in which the customer expects more and more of a product or service.

The Benefits Of Data Analysis

Data analysis helps to monitor and control business processes. Data provide information about which product was successfully sold and why or why a certain product was not sales-generating. Data analysis has been shown to increase sales and enable faster, more informed decisions to be made. It helps to analyze the right target group for a company, to build up a customer base, and ultimately to establish successful customer loyalty. The acquisition of data about previous and future business activities gives companies a decisive competitive advantage.

The first step is to define which data should be used and how. As a rule, a company uses internal data to a large extent, which is supplemented by external data. The data is then divided into groups. Which groups these depend on the business objectives. In the next step, the data is made available for analysis at a central location. This reservoir is also known as a data warehouse (DWH or DW for short). In this technical process, data elements from source databases are compared with the warehouse. All data is mapped to both a source and a destination and, using formulas, is converted into data formats that meet the requirements of the data warehouse.

Correct Analysis Of Different Types Of Data

Newer systems can analyze structured data efficiently, but with conventional or older systems the user reaches his limits since these are not designed to extract information from unstructured data. In order to give the data depth and context, a division between structured and unstructured data is required. You organize structured data in a relational database in such a way that it can be easily processed and edited. Examples of structured data are phone numbers, postcodes, currencies and etc. This data usually reflects the past, which is great for historical analysis. Unstructured data includes things like e-mail, social media posts, article, satellite images, sensor data and etc.This data can be stored in a non-relational database. Unstructured data better reflects the present and can thus contribute to it to forecast future developments.

Once the data is collected, validation occurs to identify and fix data quality issues that can affect the quality of the analysis. In addition to data profiling processes to ensure that the data set is consistent and complete, validation also includes data cleansing processes that help eliminate duplicate information and errors. In the final step, the data can be analyzed using a data visualization tool to reveal hidden correlations, patterns, and trends that can be used to guide business decisions.

Qualitative Data Analysis and Quantitative Data Analysis

Before you methodically evaluate your data, you should first determine which empirical result you want to achieve. In qualitative data analysis, you examine individual cases in detail in order to evaluate them interpretively. Often, open questions are dealt with here. Quantitative data analysis, on the other hand, aims to collect as many results as possible in order to evaluate them statistically. This includes the univariate and multivariate data analyzes.

In quantitative analysis, a distinction is made between univariate data analysis and multivariate data analysis. While you only analyze one variable (characteristic) in a univariate data analysis, you consider two or more variables at the same time (simultaneously) in multivariate data analysis. However, this application only makes sense if there are structural, mutual dependencies between the variables or relationships between the objects (feature carriers).

The 7 Methods Of Data Analysis

Dates can be descriptive or predictive. A company can choose one (or more) of these types based on their own stage of development or their own decision-making processes. However, data-driven organizations that need to make quick decisions are better off using predictive or prescriptive data.

1. Descriptive data analysis makes it possible to combine raw data from multiple data sources in order to gain valuable insights into the past. However, the results are not well-founded. It simply determines what is wrong and what is right without explaining why it is so. For this reason, data-driven companies usually use the descriptive analysis of data in combination with other methods.
2. Diagnostic data analysis shows what happened in the past and why. It is possible here. Clarify causes and effects, analyze consequences and identify patterns. Companies choose this method to gain in-depth insight into a specific problem. A popular application is the analysis of the success of a product or service. On the basis of well-founded diagnostic data, you can decide whether a product will remain in the range or whether you want to replace it with a new one.
3. Predictive data analysis is particularly useful for showing the probability of failure in certain situations. Based on the current data, you predict what will happen in the future. This method makes it possible, based on the results of descriptive and diagnostic analyzes, to determine tendencies and to identify deviations from normal values at an early stage, and predict future trends as precisely as possible. Examples of areas of application for predictive data analysis are customer behavior, device failures, effects of weather on sales, fraud detection, marketing campaign optimization, credit scores and etc.
4. Prescriptive data analysis is an increasingly popular method in modern data analysis. This is not just about the current data situation, but also about the exploitation of the potential of the data in connection with the determination of new trends. Example: A company was able to identify some opportunities for repeat purchases in its CRM system using customer analytics and the sales history.
5. Data mining is a form of advanced analysis. Unstructured data is turned into useful information such as patterns, correlations, and anomalies.
6. Artificial intelligence (AI) and machine learning (ML) are also considered advanced methods of data analysis. AI is a computer’s ability to process information in a human way; B. to understand and answer a question. ML refers to a computer’s ability to program itself. AI and ML are a powerful combination with which you can optimize the data analysis process due to almost complete automation. This includes finding new data sources, structuring data, and suggesting new approaches.
7. Big data analysis focuses on extending traditional business intelligence and enabling reporting. These are based on online analyzes of analytical data (OLAP), which enable trend analyzes as well as advanced analyzes such as predictive and prescriptive data analysis. The bigger big data gets, the more tools and techniques are being used online to make the process easier and more efficient.

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Whether developing products or optimizing production processes, creativity is required in the job. Design thinking can help to find new solutions. For many people, design thinking is synonymous with brainstorming, post-it, and imaginary personas based on confused ideas. But that’s not true. Those who get involved in the creative work process can learn a lot about themselves, product development, and working in a team. Put yourself in another professional area: Think like a designer, why not? In addition to the approach of an engineer, completely new aspects can arise that advance your project. Design thinking is about adopting ways of thinking from creative professions. The goal is to systematically produce creative ideas and innovative products. How does a designer think?

• Designers are deeply concerned with products, services, and the user experience
• The focus of designers is on people
• Designers work with prototypes and test them on users
• Work processes do not run in a straight line but in loops
• Errors are welcome and expedient

In this way, designers eliminate the weaknesses of a product. Engineers can use these approaches to think outside the box on the one hand, but also to really create products that people need on the other.

How Does Design Thinking Differ From Traditional Methods of Product Development?

There is no uniform definition for Design Thinking, but the basic principle is always the same: An interdisciplinary team is put together for the process that combines different areas of expertise. The focus is on the needs of the customers. The entire development is based on them, from the first idea through the prototypes to the finished product. At first glance, this may sound familiar to most engineers. In practice, however, the methods differ significantly from the typical approach in companies:

In design thinking, the interdisciplinary team is involved in the entire development process and not, as usual, only selectively when it comes to topics from their own specialist area. The needs of the customers are not obtained through marketing analyzes or statements from the sales force. Instead, the team goes straight to the customer who later also assess the first prototypes. The different phases mean that creativity is not restricted at the beginning by counter-arguments such as feasibility or profitability. There is a focus on the development process, which otherwise can rarely be implemented in everyday work. Design thinking means that the creative processes in the company are given space and time to make them possible at all. In this sense, it is not just a method of product development, but a change in corporate culture.

How Does Design Thinking Work?

Design thinking begins with an examination of how one’s own workspaces encourage instead of hindering creativity and teamwork, and how team success can be placed above individual performance. But also with how products are developed that users really need and inspire. As an engineer, you should take a step back and put the success of the team above your own concerns.

• Phase 1: How Does the Design Thinking Process Start?

If the team comes together for the first time, the first step should be to clarify the framework conditions for cooperation, for example: There are no hierarchies during the design thinking process. The word of the trainee carries just as much weight as that of the head of the development department. Everyone lets other interlocutors finish speaking. Nobody expresses themselves negatively or even disparagingly about ideas and suggestions.

Here’s a tip: In design thinking, new impulses also arise from breaking out of the usual structures. However, many employees and managers find it difficult to abandon hierarchical thinking and see themselves as team members on an equal footing. Small changes can make it easier to rethink. For example, it makes sense for all team members to wear casual clothing. A different seating arrangement can also be advantageous – managers should never sit at the head end. Once the rules have been clarified, it is important to define the topic or problem for design thinking.

• Phase 2: Design Thinking Doesn’t Work Without The Customer

Many engineers are familiar with the phenomenon that a sophisticated product comes onto the market that looks good and works perfectly but hardly anyone wants it. Because the needs of customers do not always match the ideas of product developers and marketing experts. An important process in design thinking is therefore the actual wishes of the future users get to know. It starts with the team members taking a little distance in design thinking and asking themselves what they would like for themselves as users.

This phase should also include making contact with potential users outside the team. This is not about marketing studies, but about real experiences and opinions. So they don’t have to be asked the representative for Design Thinking. Brief interviews with colleagues, family, friends, customers, and business partners, for example, can be informative. The results are then presented to the team and compiled for all to see, for example using index cards with keywords, drawings, icons, and the like. As a rule, clusters on various topics quickly form so that the priorities of the users become clear. These needs remain visually in the room during the entire design thinking process. Additional flipcharts or magnetic boards should therefore be available for further phases.

• Phase 3: Allow Creative Ideas For Design Thinking

In this phase of design thinking, creativity is required. Even the most absurd ideas are welcome. Because under certain circumstances, these are exactly the approaches that can later be broken down into a completely new solution. Thoughts, drafts, small ideas, big concepts everything is initially only collected and in no way evaluated. Because the meaning behind the Design Thinking method is, among other things, to get involved in the thoughts of colleagues from outside the field in order to gain a new perspective. In principle, the process of collecting ideas should not take place in a single meeting, but in several meetings at appropriate intervals. So every member of the design thinking team has the opportunity to let the topic and the needs of the customers sink in and maybe come up with a good idea while jogging or in the shower.

• Phase 4: Prototyping As A Test Run Of The Ideas In Design Thinking

Of course, in the process of design thinking, ideas arise that have to be discarded because they are actually not technically feasible or, for example, the legal framework does not allow a corresponding application. Feasibility and profitability must be checked, but always from the point of view:

Could the implementation succeed despite the difficulties?

How can economic production be realized?

So it must not happen that counter-arguments are discussed until all ideas have been rejected. Instead, team members choose the most interesting approaches and begin prototyping.

In contrast to conventional development processes, however, design thinking should be about greatly reduced prototypes, i.e. rapid prototyping (rapid production of prototypes). The first thing is to grasp the principle of the approach. Basic shapes can be made from wooden sticks, cardboard, or modeling clay. If necessary, a simple simulation on the computer makes sense, but this should only be done additionally. Because touching and possibly even trying out the products is an important aspect of design thinking.

• Phase 5: Present Prototypes To The Customer

The team members first test the simply made prototypes themselves. Could the principle work? Does it correspond to your ideas about the solution? Would you buy it? In this phase, of course, prototypes are sorted out. It may be possible to tinker again immediately afterward. If prototypes have been developed that are to be further elaborated in the design thinking process, the next step is to see the potential customer. As with the basic idea, the immediate environment can be included in order to show the prototype and obtain opinions.

Then the members of the design thinking team come together and exchange ideas about the results again. If these were already positive, the suggestions are taken up and the prototype adapted. If necessary, it may also be necessary to start further ahead and return to the creativity phase in order to collect ideas again after the customer feedback and to build further prototypes. Design thinking is an iterative process , the processes are repeated until the desired result is achieved.

Design thinking always requires a profitability analysis. It must be clear whether your own area has any need for innovation at all. Time and resources must be invested in the process itself. The company’s management needs to stand behind this method and provide four to six people. If you can afford these resources, nothing stands in the way of creative work. Design thinking encompasses a creative, playful, and hierarchy-free new work culture and that is far more than describing colorful post-it.

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What is customer segmentation?

Customer segmentation is the process of dividing customers into sub-groups based on common characteristics so companies can market to each group effectively and appropriately. At the same time, it is an essential tool that aims to save companies from the high costs of mass marketing targeting everyone who is not in the target audience, by getting to know the customer better and quickly following customer trends, by going to the right target audiences with the proper methods. Data-driven segmentation, on the other hand, is to analyze customers and potential customers using accurate and quality data.

Why does it matter to segment customers?

Segmentation allows using of time, money and other resources in a more efficient way. By segmenting the market, companies provide information about their customers. When the analyzes are made according to the determined customer groups, campaign and marketing strategies are created more soundly.

Many benefits can be achieved if customer segmentation is done correctly. For example, the best customer segmentation exercise available can tangibly impact your business results by doing the following:

1. Increasing revenue with segment-based marketing
2. Improving customer service quality by getting to know customers better
3. Better product development by anticipating needs
4. Developing the different marketing strategies for each segment
5. Better communication with customers

Types of Customer Segmentation

There are many ways to segment customers. But first of all, it should be known that a single method cannot be used for grouping. The segmentation method should be chosen according to the type of work and customers.

Here are the several  types of customer segmentation:

• A Priori Segmentation
• Needs-based Segmentation
• Value-based Segmentation
• Demographic Segmentation
• Technographic Segmentation
• Geographic Segmentation
• Behavioural Segmentation

Which is the best method?

The answer to this question varies depending on the type and purpose of the application. We make it easier for companies to reach their goals with data-driven models specially developed for marketing by Growth Art. Offering advanced analysis tools supported by AI Technology, GrowthArt also takes into account the spatial dimension in order to understand all segmentation types easily and effectively.

The creation of all the mentioned segmentation types using realistic and quality data will provide a lot of benefits to the companies. All these types of segmentation contribute to obtaining invaluable results. However, these data should be used considering the purpose of the applications to be made and the types of applications to be made. Accurate data to be used in accordance with the purpose will enable companies to reach their targeted customer segment both fully and in a much easier way.

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Business Intelligence (BI) refers to processes, technologies, knowledge, and applications that are used to make well-founded, data-driven business decisions. BI includes data collection, data aggregation and analysis, and meaningful presentation for effective decision-making.

Data-driven organizations use different BI tools to access historical data and real-time information in a data repository. These are used to carry out queries, create individual reports, and predict future developments. The tools include advanced analyzes performed by trained data scientists as well as insights generated independently by machine learning algorithms. Data repositories for BI applications include data warehouses (centralized or decentralized), production databases, operational data stores, and data marts. In data analysis, however, data sets are analyzed in order to gain knowledge from them. There are two types of data analysis:

1. Predictive Analysis: This involves analyzing historical data to determine the most likely outcome.
2. Prescriptive Analysis: This involves running hypothetical scenarios to determine the most likely outcome of a particular action.

Data analysis is an essential part of BI and BA, but only part of the overall system. BA is a similar but separate process with a different function. It collects historical data on trends and insights to drive business change. BI, on the other hand, uses historical data and real-time information to support current decision-making: that is, to evaluate what works and what does not and then to decide on the best course of action. While BI primarily supports current business decisions, BA is mainly used for future prognoses.

What Are The Functions Of Business Intelligence?

Business Intelligence (BI) comes in the aim of which is to support decision-making in companies. Although Business Intelligence (BI) has now established itself on the market, there are still different views as to how the term should be interpreted. The first definition for BI is a process that transforms data into information and this in turn through the application of experience into knowledge. This classic approach consequently includes all processes and systems with which market, competition, and company data can be systematically analyzed. Another definition describes the BI from an IT-based and company-specific point of view. According to this definition, BI means the decision-oriented collection, processing, and presentation of business-relevant information. A dashboard is generally used for data visualization. The following aspects are included in this definition:

• Decision-making
• Collection of data using data mining methods
• Preparation of data
• Presentation of business-relevant information

The aim of Business Intelligence is to generate economic knowledge about the current status and future perspectives of both your own company and the respective business environment.

Business Intelligence In A Wide Range Of Companies

In the meantime, BI is used not only by large companies but increasingly also by medium-sized companies. BI simplifies the application significantly. Self-Service BI enables users without any knowledge of statistics to analyze the data obtained. With self-service BI, simple inquiries are sufficient to carry out analyzes.

As an analytical information system, BI is based on the operational company systems. The area of application of a BI system extends across the entire management level and reaches upper, middle, and lower management. In general, the support provided by the BI system is based on Online Analytical Processing (OLAP). This makes the data immediately available for reporting. Since several dimensions are displayed simultaneously using an OLAP cube, they can also be combined with one another for analysis.

The use of business intelligence for decision support is not a one-time project, but an ongoing cycle, which consists of several phases. During the first phase, the collected data is quantified and qualified. This means that the data quality of the data is recorded and the data is then analyzed using mathematical methods. The results of the analysis are then used to derive insights that support the business processes. The next phase is concerned with evaluating these findings. The assessment is based on the company’s objectives. In the last phase, the findings are implemented in concrete measures. These conversions in turn provide the input information for the next BI cycle.

Business Intelligence Tools: 7 Key Features and Functions

Efficient business intelligence requires the right tools. There are several types of BI tools designed for different parts of the overall BI process and based on different standards. These tools can be used on their own or as part of an integrated product suite.

• Online Analytical Processing (OLAP): BI tools that are used to analyze large amounts of historical data with a drill-down function. The information obtained is stored in OLAP cubes – i.e. with a multidimensional view of the data – and made accessibly.
• Reporting: BI tools that visually present data such as charts, maps, and graphs extracted from a query. The benefits of BI reporting tools include the speed, efficiency, and accuracy of reports.
• Advanced Analytics: BI tools used by data scientists in building predictive and prescriptive analytical models.
• Operational BI: BI tools that process incoming files in real-time and thus ensure high visibility and transparency as well as faster access to information for decision-making. With real-time data and insights, a company can react quickly to market trends and events.
• Open-source BI: BI tools that were developed with open source code can be adapted as required. These tools usually come as a suite of products with built-in reporting and analysis functions.
• Self-service BI: BI tools that do not require training in statistical analysis or data mining. Self-service systems are configured so that users can query, generate reports, and gain valuable insights using interactive dashboards.

Why Is Business Intelligence So Important?

Modern business decisions are playing an increasingly important role due to the increasing accessibility of big data, but they are also increasingly difficult to make. An enterprise data warehouse often contains a terabyte or more raw data that needs to be processed and prepared for analysis. BI systems enable comprehensive data analysis, often within a few minutes, in order to react to specific business requirements. The advantages of BI at a glance:

• Faster response time: In-memory analyzes with cloud-based data warehouse solutions can analyze data in real-time and thus provide fact-based information in minutes.
• Better business decisions: BI extracts facts and transforms data into meaningful and reliable information.
• Improved operational efficiency: BI makes the change relationships between different components of the business are more transparent. This allows problems and inefficiencies to be identified and addressed more quickly.
• Higher ROI: BI helps to identify the resources required to achieve set goals, increase productivity through accelerated data analysis, and to develop new sources of income.
• Faster Reporting: BI enables real-time reports with up-to-the-minute, accurate data sets that give you a competitive edge in solving complex business problems.
• Precise Strategies: BI helps identify key data trends and patterns. This is a great way to prioritize and allocate resources to get the results you want.
• Satisfied Customers: BI provides data on KPIs that improve key business functions (product or service optimization, faster time to market), resulting in a higher customer satisfaction rating.

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In today’s busy digital marketplace, it is important to gain the trust of prospects and customers. This helps companies build a positive brand reputation. If the content is engaging, educational, and valuable, readers will think the same things about the company. The more value companies can achieve with content, the easier it is to build trust with both target groups. If articles appear regularly across platforms, the company is quickly recognized as an industry leader. Publishing content through third-party vendors or influencers can further build trust in the target market.

Content marketing offers conversion rates that are higher than other digital marketing methods. According to surveys, video content in content marketing can help achieve a high ROI and significantly increase conversions. Marketers are doubling the average website conversion rate thanks to content marketing efforts. In order to optimize content for conversions, you need a clear call to action. The call-to-action (CTA) takes the reader to the next step in the conversion process.

Optimized Content Supports Search Engine Optimization

The affordability of content marketing makes it an important strategy especially for small businesses looking to maximize their sales. This makes content marketing an affordable tactic with a great ROI. Content marketing efforts produce three times as many leads as paid search ads. The key to maximizing content ROI is creating a content marketing strategy that takes the target market into account.

Present Specialist Knowledge With Content

Consumers want to know that they are dealing with experts who understand the ins and outs of the industry. Content marketing enables the brand to demonstrate expertise in its field while providing valuable knowledge so readers can make an informed purchase decision. Customers will remember the brand that provided them with relevant information and solutions.

Content Builds Long-Term Relationships

With content marketing, companies can build lasting business relationships. Quality content is an essential part of building trust and building a positive brand reputation. A loyal customer is on average worth up to 10 times more than an impulse buyer. When repeat sales are a significant part of sales, relationships with current customers should be strengthened. Content that adds value to the customer base helps increase brand loyalty by strengthening relationships with customers. Not only are consumers more positive about a custom content company, but they also feel closer to that brand. The more value they get, the more likely they are to come back. By helping consumers make smarter buying decisions, companies are working to build further trust and drive repeat sales.

The majority of internet users want to learn more about businesses through custom content. With content marketing, data that is already available from the target group can be effectively played out. User-defined content appeals to the target group and offers individual values. Entertaining and educational content shows what your brand is about. The key to creating an effective content that businesses can connect with their audiences is to focus on topics that are relevant to the needs, interests, and challenges of the target customers.

Stand Out From The Competition With Content Marketing

In the busy digital marketplace, companies must stand out from the competition. Content marketing is especially effective for little ones. Companies to stand out from others in the industry and to show what makes the company so special. The themes and value of the content provided can make all the difference to a consumer. With the help of their brand voice, companies develop unique and appealing content. This brand personality can help connect with target customers and show consumers what makes the company so special. Content marketing plays a role at every stage of the marketing funnel:

• Top-of-Funnel (TOFU): At the top of the funnel, potential customers are only just becoming aware of problems. You also become aware that there are solutions. In this phase, content can help inform them.
• Funnel middle (MOFU): In the middle of the funnel, leads evaluate their options for products or solutions. The content at this stage of the funnel is intended to educate leads about their various options so that they can narrow down their choices and move closer to a final buying decision.
• Hopper Bottom (BOFU): Once prospects reach the bottom of the hopper, they are ready to convert. The content at this stage is intended to give the buyer one final boost to encourage and empower them to make an informed purchase decision.

Content marketing plays a role at every stage of the marketing funnel and what types of content are best for top, middle, and bottom leads. The content should reach potential customers in every phase and take into account the requirements of the individual leads at this point.

Further Strategies In Online Marketing

Every company needs to create engaging, high-quality content as part of its marketing campaign. For example, let’s say marketers want to launch a lead promotion email marketing campaign. This requires email content that is engaging and encourages readers to meet the next step to do. Content that has already been created can also be used to nurture these leads. Content marketing is integrated into all other areas of digital marketing strategy. It is the glue that holds the campaigns together and ultimately contributes to their success.

In content marketing, high-quality information is provided without the advertising purpose being noticeably in the foreground. At the top is the usefulness for the target group. It should not only inform but also inspire and entertain the target group through content that is relevant to them. It also serves to build a brand and should also create a product-independent, positive image. When creating the content, the strategic direction of the company, the media used and the target group must be taken into account and kept in balance so that the desired effects occur and the added value for the target group does not go under.

With all of the benefits outlined above, it’s easy to see why content marketing is important to businesses of all sizes. Anyone wanting to see the importance of content marketing in action should work on a content marketing strategy that puts the target customers at the center. We would be happy to take over the conception and implementation of your content marketing. Upon request, we will take care of the content, produce and publish it in images, text, and video in all shapes and colors.

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