Discover in this article the synergy of Predictive Analytics and CRM, where data-driven insights transform customer relationships. Delve into the world of predictive possibilities, from deciphering customer behavior to fortifying brand loyalty. Explore the intersections of data, technology, and human expertise, empowering businesses to predict, personalize, and prosper in the digital age. Read and discover
Why Predictive Analytics and CRM Improve Sales
In this article, you will learn about Predictive Analytics in the context of CRM (Customer Relationship Management) and how you can leverage these two areas for your benefit.
We will explain what Predictive Analytics means in the context of CRM and how you can connect these two areas to make them work for you, allowing you to understand and deepen relationships with your customers.
In recent years, users have increasingly recognized the value of their data. This realization should also prompt businesses to rethink their strategies to maintain success.
The use of mathematically driven Predictive Analytics and the derived strategies played a significant role in helping FC Liverpool regain their position at the top of football.
Predictive Analytics Definition
Traditionally, for example, marketing relies on more descriptive statistics that explain the past in an attempt to derive intuitive behavior from it. For instance, "Chips and watching football work well, so let's promote Chips." We can see that this approach is not data-driven but rather based on gut feelings.
But what is Predictive Analytics? Predictive answers the question, "What might happen in the future?" And Predictive Analytics uses a significant amount of relevant data, including Big Data, to derive predictions. This includes the likelihood of a specific event, forecasting future trends, or outcomes. In our case, it allows companies to anticipate and respond to future market and customer movements.
To achieve this, past data from the actions of companies, their outcomes, and user behavior are analyzed and evaluated. Patterns and relationships between user groups can be derived from this data. Based on this, the data is combined and analyzed with statistical methods, mathematical models, or machine learning, including Artificial Intelligence (AI), along with present information to make predictions about future behavior, events, trends, and outcomes. This enables informed decisions, actions, and forecasts to be derived.
What is Predictive Analytics in the context of CRM?
Predictive Analytics, in the context of Customer Relationship Management (CRM), elevates the use of CRM to a much stronger level. By leveraging CRM and other data, it allows for predictions about customer behavior and preferences, as well as patterns in customer data, which can be utilized to enhance customer relationships.
In this process, data such as purchase history, demographic information, and data related to interests and interactions are used to segment users and customers. This utilization of first-party data enables a 360-degree view of the customers. When Predictive Analytics is integrated into a CRM system, data-driven decisions can be made, and from the segments, behavior and preferences of customer segments can be more accurately forecasted and managed.
In the combination of Predictive Analytics and CRM, the objectives are to develop value-based customer relationships and minimize churn. In the future, it will be essential to offer users clear added value for their data, as they are increasingly aware of its value. Predictive Analytics enhances the intelligence of CRM and is now an integral part of modern CRM strategies. However, 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 CRM's strategy and objectives, the integration of Predictive Analytics can quickly yield significant advantages, as it elevates the CRM to a new and comprehensive level. It facilitates the connection between Customer/Client Relationship Management, Product Management, and Brand Management, enabling businesses to steer relevant interactions for customers. For example, Netflix successfully utilized Predictive Analytics in promoting the hit series "Stranger Things" by engaging various user groups with highly personalized streaming content.
Overall, integrating Predictive Analytics with a CRM system enables businesses to gain a deeper understanding of customer behavior and preferences, leading to various CRM benefits such as improving customer relationships, increasing repeat purchases, and driving revenue growth, including short-term sales forecasting.
Predictive Analytics can be utilized through Customer Relationship Management measures to optimize resource allocation, such as sales representatives and marketing budgets.
By identifying the most profitable customer segments and addressing them accordingly, businesses can enhance customer loyalty. Additionally, at-risk customers can be identified, allowing proactive measures to retain them, which becomes increasingly crucial considering rising customer acquisition costs. For example, if users regularly ordered their weekly water supply from a beverage delivery service but suddenly stop for several weeks, various reasons like summer, holidays, illness, or relocation could be factors. Reactive measures, such as offering vouchers, can help regain these users or gather new information, especially after a relocation.
This contributes to increasing Customer Lifetime Value (CLV). CRM projects can be amortized more quickly or generate stronger sales and revenue in a shorter period. Employees gain an essential tool for modern marketing and customer management, leading to improved customer engagement through pattern utilization and customer interaction predictions. Consequently, the overall customer experience (CX) and customer satisfaction improve. A measure of this is the personalization of content, product offerings, and pricing. By connecting content data-bases (.g. Notion) with predictive analytics and CRM it is possible to lift the power of content marketing.
How can Predictive Analytics be utilized with a CRM system?
The utilization of Predictive Analytics depends on the specific situation and infrastructure of each company. For start-ups or companies without a CRM system, it is recommended to introduce both systems simultaneously. However, existing CRM systems can be connected with Predictive Analytics to create modern Customer Data Platforms (CDPs).
In practice, the first step should involve a clear analysis of the current situation in conjunction with project and CRM objectives, requirements (technology and data), expectations, and relevant parameters.
To successfully introduce and utilize Predictive Analytics in conjunction with CRM, the following prerequisites should be met:
- Compliant data collection and processing of large data volumes in nearly real-time
- Establishment of necessary data and analysis layers, potentially using machine learning algorithms (including neural networks and AI, if applicable)
- Analysis of existing data and transformation of customer data into useful customer segments and behavioral targeting for application
- Integration of additional tools to apply discovered patterns and insights into methods, measures, and automated campaigns
- Ensure robust and high-performance interfaces (APIs) to other subsystems (e-mail marketing, personalization, supply chain, content planning, etc.)
- Integration of customer service
During the implementation and ongoing successful use of Predictive Analytics CRM, the Customer Lifetime Value (CLV) and Customer Lifecycle are central elements and also pose one of the greatest challenges. The (Predictive) CLV is the net present value of all future "revenues" of a customer, minus all associated costs.
The Customer Lifecycle represents the efforts to build and maintain the relationship with customers through CRM and high levels of service. Predictive Analytics can generate flywheel effects in this process. It is essential to consider whether it is the first or a repeated cycle between a user/customer and the company. In early stages of a young relationship, applying personalized or even hyper-personalized measures may not be advisable as it can quickly be off-putting. Higher success probabilities lie in measures that contribute to trust and a positive CX.
To simplify, some general examples are provided:
- Awareness – Marketing campaigns for specific customer segments based on predictions of behavior and interests during the attract phase
- Consideration – Relevant information, guides, personalized content and recommendations, relevant/personalized search results, reviews and ratings, reviews to transition to the engage phase
- Purchase – Supporting the purchasing process based on preferred timelines and channels, e.g., controlling pricing and promotions, relevant communication channels, relevant services - goals are customer acquisition and relationship building
- Retention – Providing relevant help, support, after-sales services, and advice for customer segments to meet their needs, collecting specific feedback for continuous optimization - goals are customer delight and retention
- Advocacy – Enabling and empowering loyalty measures, reward systems, encouraging customer referrals within their communities, and transitioning to a new cycle through segment-specific/personalized measures
Additionally, it is crucial to consider challenges at the decision-making level and investment readiness based on a fundamental understanding of objectives and potential successes for all stakeholders.
What challenges exist in implementing Predictive Analytics with CRM systems?
The challenges on the path to becoming a Predictive Analytics company, as with all data and technically oriented projects, lie in three essential areas:
- Data - Availability & Infrastructure
Predictive Analytics relies on large volumes of data, and its quality and quantity are crucial. Inaccurate or inconsistent data can affect predictions and lead to faulty Customer Lifetime Value (CLV) calculations. Ensuring the quality of existing CRM data is essential. Multiple data entries or significant gaps in data collection must be cleansed and avoided.
When introducing Predictive Analytics, it is essential to place a strong emphasis on building the fundamentals of tracking and data collection while adhering to data protection guidelines. Like any work with personal data, data collection and processing must strictly adhere to data protection and data security laws. Ensuring that data is collected and stored securely is crucial.
- Technology Stack and Development
Integrating Predictive Analytics with a CRM system can be complex and may require technical expertise, leading to difficulties in effective collaboration between the two, as well as other connected systems. Does the technology stack have modern interfaces, and is communication between the systems clear and unambiguous? Are the connections robust? Do the systems have sufficient storage and processing capacities? Do the systems offer user-friendly applications? These and other questions need to be addressed.
- Personnel - Access, Training, Expertise
To ensure clean and successful work with Predictive Analytics and CRM, it requires building a well-trained and motivated workforce (internal/external) with good expertise and continuous development.
As CRM systems, Predictive Analytics, and their projects are at the intersections of digital transformation, a significant challenge lies in integrating cultures and guiding changes in processes and workflows.
Cross-functional teams consisting of experts and specialists provide a solid foundation in this regard.
Additionally, challenges in decision-making paths and investment readiness should be considered based on a fundamental understanding of objectives and possible successes from all stakeholders' perspectives.
Companies that already have a CRM system typically face more significant challenges in upgrading it with Predictive Analytics. Compared to companies without legacy systems and their associated burdens, those without such systems have a clear advantage in terms of implementation time, project complexity, and outcomes.
Which industries benefit the most from using Predictive Analytics with CRM?
Predictive Analytics combined with CRM can and will be employed in almost all industries and sectors where predictions or pattern recognition in data are required to derive CRM measures. Examples include:
- Banking & Finance: Identifying potential fraud, predicting credit risks, and forecasting financial performance.
- Transportation: Optimizing logistics and supply chains, predicting maintenance needs, and enhancing fleet performance.
- Healthcare: Identifying patients at risk of developing specific diseases and optimizing patient treatment.
Predictive Analytics in sales, whether B2B or B2C, is used to develop forecasts and measures for acquiring and retaining customers through optimized customer relationship management. Predictive Analytics is also used for revenue forecasting and inventory optimization.
- Furniture Industry: Providing relevant and customer segment-specific consultations on furnishing styles, high-level services related to matching furniture items, and follow-up purchases.
- Fashion Industry: Transitioning initial interest generated from influencer marketing into a community with close, intensive customer relationships. The specific applications of Predictive Analytics can vary depending on the industry, but the fundamental principles and techniques are generally similar.
Examples of companies successfully implementing Predictive Analytics with their CRM systems
The successful use of Predictive Analytics in CRM can be seen in many companies. Some examples of companies enhancing customer retention with the help of Predictive Analytics in CRM include:
- Spotify: Personalizing music, podcasts, and other content for users through their algorithms and recommendations, contributing to higher customer engagement and satisfaction, and achieving a unique selling point.
- Uber: Using Predictive Analytics to optimize their ride-hailing service, including predicting demand and driver behavior, and improving overall customer experience.
- Lufthansa: Leveraging Predictive Analytics to optimize their flight schedules based on customer behavior, interests, and demands, as well as enhancing and personalizing marketing and customer service efforts.
- Douglas: Employing Predictive Analytics in conjunction with CRM and customer loyalty programs to personalize marketing and customer retention programs and optimize store operations, which can double Customer Lifetime Value, especially in transitioning to omnichannel customers.
- In the furniture industry, porta.de doubled customer retention within a year and significantly increased Customer Lifetime Value through a combination of Predictive Analytics and eCRM.
Tools for implementing Predictive Analytics with CRM systems
From a technical perspective, the key factors for implementing Predictive Analytics CRM solutions involve data collection, data analysis, segmentation, providing predictive and recommendation outputs systemically, and CRM handling of these outputs along with the channels it manages.
As previously described, an evaluation of prerequisites (technical, monetary, etc.), requirements (functions, investment and cost volumes), objectives (economic, user-friendliness, application areas), and all relevant framework conditions is crucial on the path to becoming a Predictive Analytics company. In the current development we do see great chances to build first parts or MVPs, to test and learn, with low-code development platforms.
The tool landscape ranges from loose systems, integrated best-of-breed solutions, and comprehensive marketing cloud providers to complete in-house developments. In recent years, Customer Data Platforms (CDPs) - including/excluding Customer Engagement Platforms (CEPs) - have emerged. CRM system providers have also made significant advancements and integrated Predictive Analytics. Best-of-breed and in-house developments have the advantage of being individually tailored to the needs, capacities, and current development status of the company, allowing for scalability.
There are numerous tools that allow the combination of Predictive Analytics with Customer Relationship Management (CRM) systems. Here are some examples, ordered from more best-of-breed to comprehensive cloud providers:
- Snowflake – Semi-structured data in predictive modeling.
- Tensorflow – Machine learning from customer data.
- Alteryx Predictive and SAS – Data analysis platform for working with customer data, predictions, and integration with CRM systems.
- Microsoft Azure – Cloud-based platform with a variety of tools for data analysis, machine learning, and integration with CRM systems.
- CrossEngage – Customer Data Platform & Customer Prediction Platform for AI-driven target group and customer segment creation and automated activation.
- Segment – Comprehensive Customer Data Platform with built-in analytics and segmentation and distribution capabilities.
- Salesforce – Including Einstein for Predictive Analytics in conjunction with the Marketing Cloud, offering an all-in-one solution.
For successful, targeted, and relevant Customer Relationship Management measures, the use of Predictive Analytics is essential. It allows businesses to significantly increase their understanding of customers and minimize entrepreneurial risks. With Predictive Analytics, businesses can better understand their customers from the start, make predictions, and better control their actions and capacities.
The implementation of Predictive Analytics CRM does not need to be accomplished through endlessly large, expensive, and time-consuming projects; it can also grow progressively and efficiently through a skillful combination and scalability of subsystems.
In conclusion, here are some quick wins:
- Understand initial target groups and apply targeted marketing measures, even without immediate personalization, through e-mail and social media marketing. This will quickly and cost-effectively increase engagement and conversion rates.
- Segment customer bases based on behavior, preferences, and demographic information to improve target audience engagement.
- Use churn predictions to identify customers at risk of leaving and try to retain them through personalized customer retention campaigns.
- Use recommendations, reviews, and guide content to improve up-selling and cross-selling for specific segments using Predictive Analytics.
And most important: Have fun building, testing, and learning.