Why Predictive Analytics and CRM Improve Sales

1. Predictive Analytics Definition
2. What is Predictive Analytics in connection with CRM?
3. What are the benefits of using Predictive Analytics with a CRM system?
4. How can Predictive Analytics be used with a CRM system?
5. What challenges exist in implementing Predictive Analytics with CRM systems?
6. Which industries benefit most from using Predictive-Analytics-CRM?
7. Examples of companies that have successfully implemented Predictive Analytics with their CRM
8. What tools can Predictive Analytics CRM be implemented with?
9. Conclusion
10. Finally, some quick wins:

Our guest author Michael Munder explains what Predictive Analytics means in connection with CRM and how you can connect these two areas and use them to understand and deepen your relationships with your customers.

Users have increasingly recognized the value of their data in recent years. This should also lead companies to rethink their approach in order to continue to be successful.

The use of mathematically driven predictive analytics and a derived strategy has helped to bring FC Liverpool back to the top of football.

Predictive Analytics Definition

Traditionally, for example, marketing relies on largely descriptive statistics, which explain the past and attempt to derive intuitive behavior from it. Like, for example: "Chips and watching football work well, so we promote chips." As we can see, this is not really based on data, but rather comes from the gut.

But what is Predictive Analytics? Predictive answers the question: "What could happen in the future?" And Predictive Analytics uses exactly a relevantly large amount of data, up to Big Data, for this purpose, in order to derive predictions from it. This includes the probability of a certain event, the prediction of future trends or results. This allows companies in our case to foresee future market and customer movements and to react accordingly.

In doing so, past data from company activities, their results and data from user behavior are analyzed and evaluated. This allows patterns and relationships between the user groups to be identified. On this basis, this data is combined and analyzed with methods from statistics, mathematical models or machine learning, or artificial intelligence (AI for short), and current information in order to make predictions for future behavior, events and trends, as well as results. This can then be used to make informed decisions, measures, or predictions.
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What is Predictive Analytics in connection with CRM?

Predictive Analytics in connection with Customer Relationship Management (CRM), enables the use of CRM to be raised to a much stronger level. The CRM and other data allows predictions to be made about customer behavior and preferences, and patterns in customer data to be derived, which can be used to improve the customer relationships.

In doing so, data such as purchase history, demographic data, data on interests and interactions are used to segment users and customers. These first-party data provide a 360-degree view of the customers. If predictive analytics are integrated into a CRM system, data-based decisions can be made and the behavior and preferences of customer segments can be predicted and managed more accurately.

Graphic 1: Clustering of customer segments with PA (highly simplified) - Source: own illustration

In the combination of Predictive-Analytics-CRM, the objectives are to develop value-based customer relationships and to minimize churn rates. In the future, it will be crucial to offer users significant added value for their data, as they are increasingly aware of their value. Predictive analytics increases the intelligence of CRM and is an indispensable part of the modern CRM strategy, especially when avoiding CRM errors, but it must be ensured that users are motivated to share their information.

What are the benefits of using Predictive Analytics with a CRM system?

Depending on the strategy and goals of a CRM, the use of predictive analytics can quickly generate benefits, because it takes the CRM to a new, holistic level. It enables a connection between Customer/Client Relation Management, Product Management and Brand Management in order to control interactions relevant for customers. This is how Netflix managed to attract different groups of users to the streaming service with diverse content while promoting the hit series "Stranger Things".

⁠Graphic 2: Stranger Things marketing content used by Personalization and Predictive algorithms for different user groups - Source: https://netflixtechblog.com/

The integration of predictive analytics with a CRM system enables companies to gain a deeper understanding of customer behavior and preferences. This results in numerous CRM benefits, such as improving customer relationships, repeat purchases, and increased sales, but also for shorter-term sales forecasting.

Graphic 3: Predictive Marketing Applications - Source: Marketing 5.0 - Kotler, Kartajaya, Setiawan

Predictive Analytics can be used for Customer Relationship Management measures to optimize resource allocation, for example, of sales staff and marketing budgets, by identifying the most profitable customer segments and addressing them accordingly.

Top customers and their specific interest and usage attributes are selectively used to further build loyalty. It is also possible to identify customers at risk of migration so that the company can take proactive measures to keep them. This is especially important with ever-increasing customer acquisition costs. For example, if users who have regularly ordered their weekly ration of water from a beverage delivery service but have not done so for several weeks can be identified, there may be various reasons (summer, vacation, holidays, illness, moving). Reactivation measures, such as vouchers, can potentially win back these users or at least generate additional information from a new address.

This contributes to increasing the Customer Lifetime Value. CRM projects can thus be amortized more quickly or generate stronger sales and revenues in a shorter period of time. Employees are provided with an essential tool for modern marketing and customer management, as it leads to improved customer contact through the use of patterns and the prediction of customer interactions. This increases the overall customer experience (Customer Experience – CX) and customer satisfaction. One measure that is derived from this is the personalization of content, product offers, and prices.

How can Predictive Analytics be used with a CRM system?

The use of Predictive Analytics depends on the specific situation and the infrastructure of the company. For start-ups or companies that do not yet have a CRM system, it is recommended to introduce both systems at the same time. But existing CRM systems can also be connected with Predictive Analytics. This results in modern Customer Data Platforms (CDP).

In practice, the very first step should be a clear analysis of the current situation in combination with the project and CRM goals, the prerequisites (technology and data), expectations, and relevant parameters. The following prerequisites should be met for a successful introduction and use of Predictive Analytics in combination with CRM:

• Data privacy-compliant collection and processing of large volumes of data in near-real time
• Building of necessary data and analysis layers as well as possibly algorithms for machine learning (possibly using neural networks and AI)
• Analysis of existing data and transfer of customer data into useful customer segments and behavioral targeting for application
• Supplementing additional tools to transform the patterns and insights harvested into methods, measures, and automated campaigns
• Ensuring reliable and high-performing interfaces (APIs) to other subsystems (email marketing, personalization, supply chain, content planning, etc.)
• Connecting customer service

In the introduction, but also in the permanently successful work with Predictive-Analytics-CRM, the Customer Lifetime Value (CLV) and the Customer Lifecycle represent the central elements and at the same time one of the biggest challenges. The (Predictive) CLV is the net present value (NPV) of all future "revenue" generated by a customer, less all costs associated with that customer.

The customer lifecycle represents the efforts to establish the customer relationship. Customers are shown through CRM and a high level of service by the Customer Lifecycle. Predictive Analytics can generate Flywheel effects here. It is important to consider whether it is the first or a repeated cycle between the User/Customer and the company. In the early stages of a young relationship, it is not advisable to apply personalized or even hyper-personalized measures. This can quickly have a deterrent effect. More likely to succeed are measures that build trust (Trust) and general CX.

For simplification, some basic examples are shown:

• Awareness – Marketing campaigns for specific customer segments based on the prediction of behavior and interests during the Attract phase
• Consideration – Relevant information, advice, personalization of content & recommendations, relevant/personalized search results, reviews & ratings, reviews for the transition to the Engage phase
• Purchase – Accompanying the purchase processes depending on the personally preferred timelines and channels of the user, e.g., through control of pricing and promotions, relevant communication channels, relevant services - the goals here are on acquisition
• Retention – Relevant help, support, after-sale services and advice for the customer segments, to meet their needs, to collect specific feedback for continuous optimization - Winning the Delight phase
• Advocacy – Enabling and empowerment of loyalty measures, reward systems, encouragement to make recommendations in their communities, and transition into a new cycle through customer segment-specific/personalized measures

Graphic 4: Customer Life Cycle - Source: own illustration

And a look at the future: With the current enormous developments in AI, a dilemma can be solved in the future. Because now it will be possible, with the large number of customer segments generated from Predictive Analytics, to provide each with relevant and specific content.

What challenges exist in implementing Predictive Analytics with CRM systems?

The challenges on the way to becoming and being a Predictive Analytics company lie, as in all data- and technology-oriented projects, in three essential areas:

1. Data – Availability & Infrastructure

Predictive Analytics relies on large volumes of data, the quality, and quantity of which are crucial. Inaccurate or inconsistent data can affect predictions and lead to erroneous CLVs. The quality of existing CRM data  must be guaranteed. Multiple recording or large gaps in data collection must be corrected and avoided.

In any case, when you introduce Predictive Analytics, you should definitely place great value on building comprehensive and in-depth tracking and data collection - in compliance with data protection laws.

2. Technology stack and development

The integration of Predictive Analytics with a CRM system can be complex and require technical expertise, which can lead to difficulties in the effective collaboration of the two, and also other connected systems. Does the technology stack have modern interfaces and is the communication between the systems clean and unambiguous? Are the connections robust? Are the memory and processing capacities deep and large enough? Do the systems offer user-friendly applications? All these questions and more need to be clarified.

3. Personnel – access, training, expertise

Ensuring clean and successful working with Predictive Analytics and CRM requires a well-trained and motivated workforce (internal/external) to be built up and continuously trained.

Since CRM systems and Predictive Analytics and their projects are located at the interfaces of digital transformation, a major challenge is in bringing together cultures and accompanying changes, processes, and work flows.

Cross-functional teams of experts and specialists are a good basis for this.

Additionally, challenges are to be pointed out on the level of decision paths and the willingness to invest derived from a basic understanding of the objectives and possible successes on the part of all stakeholders.

Companies that already have a CRM system are usually facing bigger challenges when it comes to enhancing it with Predictive Analytics. Looking at the implementation time, the complexity of such projects, and their results, companies that are without legacy systems and thus their - often - burdens, have a clear advantage.

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Which industries benefit most from using Predictive-Analytics-CRM?

Predictive-Analytics-CRM are used across almost all industries and sectors where predictions need to be made or patterns need to be recognized in data in order to derive CRM measures, for example:

• Banking & Finance: recognizing potential fraud, predicting credit risk, and predicting financial performance.
• Transportation: optimizing logistics and supply chain, predicting maintenance needs, and improving fleet performance
• Healthcare: identifying patients at risk of developing certain diseases, and optimizing patient treatment.

In Predictive-Analytics-Sales, whether B2B or B2C, prognoses and measures are worked out to gain and retain customers, through optimized work on the customer relationship from the start. This is accompanied by the use of Predictive Analytics to predict sales and optimize inventory.

• Furniture Industry: From relevant and customer-segment-specific consultations, to interior design styles, high-level services, to corresponding furnishings and follow-up acquisitions
• Fashion Industry: Transfer of initial piqued interest from influencer marketing to a community to a closely intense customer relationship

The specific applications of Predictive Analytics may vary depending on the industry, but the basic principles and techniques are largely the same.

Examples of companies that have successfully implemented Predictive Analytics with their CRM

The successful use of Predictive Analytics Corporate CRM is evidenced in many companies. Some successful examples of companies that have improved their customer loyalty with the help of Predictive-Analytics-CRM are:

• Spotify personalizes music, podcast, and other content for users through its own algorithms and recommendations. This is one of the important secrets with which customer engagement and satisfaction were able to achieve a unique selling point
• Uber uses Predictive Analytics to optimize its ride-hailing service, including predicting demand and driver behavior, and to improve the overall customer experience.
• Lufthansa uses Predictive Analytics to optimize its flight planning based on the behavior, interest, and demand of customers. Marketing and customer service are also increased in relevance and personalized.
• Douglas uses Predictive Analytics in conjunction with CRM and customer card / loyalty program to personalize its marketing and customer loyalty programs and optimize store operations. Especially in the transition to omnichannel customers, this process enables the company to double the CLV. Douglas relies on massive data generated from the behavior of 44 million card owners.
• In the furniture industry, porta.de was able to double its customer loyalty within one year by combining Predictive Analytics and eCRM, thus substantially increasing the Customer-Lifetime-Value.

What tools can Predictive Analytics CRM be implemented with?

From a technical perspective, the most important factors for implementing Predictive Analytics CRM solutions are data collection, data analysis, segmentation, the systemic provision of predictions and recommendations, and the processing of this information by the CRM and the channels it works with.

As described earlier, an assessment of the prerequisites (technical, monetary, etc.), requirements (functions, investment and cost volumes) and goals (economic, user-friendliness, application areas), and all relevant framework conditions is decisive on the path to becoming a Predictive-Analytics-CRM company.

The tool landscape can range from loosely connected systems and integrated best-of-breed solutions to comprehensive marketing cloud providers to complete custom developments. In recent years, Customer Data Platforms (CDP) - incl./excl. Customer-Engagement-Platforms (CEP) – have developed. CRM system providers have also evolved and added Predictive Analytics. The advantage of best-of-breed and custom solutions is that they can be tailored to the needs, capacities, and development status of the company, and can grow with the organization.

There are a multitude of tools that can be used to combine Predictive Analytics with Customer-Relationship-Management (CRM) systems. Here are examples in their order from rather best-of-bread to holistic cloud providers:

• Snowflake – semi-structured data in Predictive Modeling.
• Tensorflow – Machine Learning from customer data.
• Alteryx Predictive and SAS – a data analysis platform for working with customer data, predictions, and integration with CRM systems.
• Microsoft Azure – cloud-based platform with numerous tools for data analysis, machine learning, and integration with CRM systems.
• CrossEngage – Customer-Data-Platform & Customer-Prediction-Platform for AI-supported creation of target groups & customer segments and automated activation.
• Segment – comprehensive Customer-Data-Platform with built-in analytics and segmentation and activation capabilities.
• Salesforce – including Einstein for Predictive Analytics in conjunction with the Marketing-Cloud forms a one-shop solution.

Conclusion

For successful, targeted, relevant Customer-Relationship-Management-Measures the use of Predictive Analytics is essential. With it, you can significantly increase the intelligence of your company and your work with customers, as well as minimize the business risk. Because you understand your customers better from the start, are able to make predictions, and better manage your measures and capacities.

The implementation of Predictive-Analytics-CRM does not necessarily have to be done through infinitely large, expensive, and long-term projects, but it can also grow gradually and efficiently through a clever combination and scalability of subsystems.

Finally, some quick wins:

• Unstand initial target groups and apply targeted marketing measures, without them being immediately personalized, through email and social media marketing. This way, you increase the engagement and conversion rates fairly quickly and inexpensively.
• Form further customer segments based on their behavior, preferences, and demographic information and improve your target group approach.
• Use churn predictions to identify customers who are at risk of leaving you. Try to keep these with personalized customer loyalty campaigns.
• Try to use recommendations, reviews, and guide content to improve your up-selling and cross-selling for specific segments with the help of Predictive Analytics.

Have fun building, testing and learning.