Data-driven marketing: How the integration of customer data boosts business success

Companies that use their data intelligently have clear advantages: they can make better decisions, create personalised customer experiences and increase their operational efficiency. However, this potential often remains untapped, not because the data is missing, but because it is fragmented, inconsistent or incomplete.

This is where data standardisation and identity resolution come into play. Those who intelligently link systems such as ERP (Enterprise Resource Planning), PIM (Product Information Management) and CRM (Customer Relationship Management) not only gain access to more information, but also improve its quality and utilisation options. This solves the problem of data silos, enables deeper analyses and promotes optimised, data-driven processes.

Challenges posed by a fragmented data landscape

Many companies use different systems that operate in isolation from one another. This leads to multiple or incomplete data records that create sources of error and inefficiencies. For example, a customer may be recorded in one system as "Max Mustermann" and in another as "M. Mustermann" with a different email address. A CRM system may only contain the contact details of a customer, while the purchase history in the ERP or their interests in the PIM are missing.

This isolated information makes it difficult to take a holistic view of the customer and prevents the data from being utilised efficiently. As a result, the potential to personalise customer experiences, optimise inventory planning or implement targeted marketing strategies remains untapped.

A practical example: The Phone House, the European trading name of the British mobile phone chain Carphone Warehouse, was faced with the challenge that its transaction systems and the web-based systems of the mobile phone providers in Spain were not optimally coordinated. This led to isolated data silos and inefficient processes. By implementing data virtualisation technology, they were able to integrate their systems, resulting in improved data quality and more efficient processes.

Data unification as the basis of a central strategy

Data unification means that information from different systems is brought together in a standardised, structured form. This is achieved not only through the technical integration of the systems, but also through the logical harmonisation of the data.

Consistency is a decisive factor here: all departments access up-to-date, coordinated data. Redundancies are avoided so that duplicate or inconsistent data records are eliminated. Automated processes reduce errors and manual effort, which leads to a significant increase in efficiency.

For example, the lack of connection between CRM and ERP, whereby VIP customers do not receive preferential delivery because their priority is not consistently recognisable in the systems. A uniform database can avoid such problems and optimise processes.

A look beyond the e-commerce sector shows that effective data standardisation is also essential in the pharmaceutical industry in order to optimise complex research and development processes. Novartis is an outstanding example of this. Novartis utilised data integration technology to combine data from internal and external sources. This enabled researchers to quickly access consolidated and searchable data, which significantly increased efficiency in research and development.

Identity resolution as a prerequisite for personalised experiences

Data unification alone is not enough. For companies to maximise the value of their data, they must also be able to create unified identities for customers, products and business processes. Identity resolution means correctly merging different data sets that relate to the same entity.

• Customer profiles become more precise: A customer can be clearly identified by various data points (email, telephone number, social media profile, purchase history).
• Better segmentation: Companies can divide customers into precisely defined target groups and address them individually.
• More accurate product recommendations: By linking purchasing behaviour, product interests and feedback, customers receive personalised offers.
• Avoidance of duplicate data records: A customer who has registered once with their business email address and once with their private email address is correctly recorded as one person.

Amazon, for example, uses advanced data integration and identity resolution techniques to provide a consistent and personalised customer experience. By combining data from various sources, such as purchase history, search behaviour and customer reviews, Amazon can present its customers with tailored recommendations and offers. Customer service also works much better if the customer's complete history is available through identity resolution, enabling personalised advice.

The segmentation of customer data is one of the most important strategies for making marketing measures, sales campaigns and product recommendations more efficient and effective. However, traditional segmentation methods often reach their limits when companies work with isolated systems.

Data standardisation and identity resolution give companies a consistent, uniform view of their customers and products. Only by combining ERP, PIM and CRM data is intelligent, dynamic segmentation possible that goes beyond simple criteria. Companies can classify their customers more precisely, identify individual purchasing habits and take targeted measures.

Dynamic segmentation through data integration

The integration of ERP, PIM and CRM provides a central basis for contextual and behaviour-based segmentation. Here are some typical scenarios:

1. Segmentation by customer value (RFM analysis)

The combination of ERP and CRM data allows customers to be clustered according to their value to the company. The so-called RFM analysis (Recency, Frequency, Monetary Value) helps to group customers into categories such as:

• High-value buyers: receive exclusive offers or premium support.
• Low-activity customers: are targeted with personalised reactivation campaigns.
• Occasional buyers: receive targeted incentives such as special offers or discounts on similar products.

2. Behaviour-based segmentation

By linking CRM (customer history), ERP (orders) and PIM (product preferences) data, specific customer clusters can be targeted:

• Customers with a regular interest in certain product categories receive personalised recommendations.
• Buyers with frequent technical enquiries receive more detailed product information.
• Customers with frequent returns are addressed with optimised product descriptions to reduce returns.

3. Geographic and seasonal segmentation

The integration of ERP data (orders & stock levels) with CRM data (customer locations) helps to identify regional preferences and seasonal trends:

• Winter sports products can be advertised specifically in regions with high demand.
• Customers in warm regions receive offers for summer products.
• Inventory information from the ERP helps to target advertising for products in stock in specific regions.

4. Combined product and customer segmentation

By combining PIM and CRM data, personalised product recommendations can be displayed based on actual interests:

• Customers who regularly buy certain brands receive exclusive new products from these brands.
• Purchasers of premium products are not advertised with budget offers.
• Customers who often buy spare parts or accessories receive automatic reminders or bundle offers.

Traditional segmentation methods are often static. Identity resolution and the intelligent linking of data enable dynamic segments that adapt automatically.

Data-driven marketing: added value through personalised experiences

The combination of ERP, PIM and CRM data creates completely new opportunities for data-driven marketing. Companies can respond specifically to customer needs and optimise their marketing strategy.

1. Personalised customer journeys

Customers today expect a personalised approach across various channels - be it by email, in the online shop or via social media.

• CRM data provides information about the customer profile.
• PIM data enables customised product recommendations.
• ERP data ensures that only products that are actually in stock are advertised.

The result: personalised experiences increase customer satisfaction and conversion rates.

2. Predictive marketing

By analysing past purchases, customer interactions and product trends, companies can predict future purchasing behaviour.

• Customers who regularly purchase a certain product category receive proactive offers for follow-up products.
• Customers whose purchasing behaviour indicates an imminent change (e.g. from entry-level models to a new model) can be offered a product in the future.
• The combination of stock levels (ERP) and purchasing trends (CRM) helps to plan orders in good time and avoid bottlenecks.

3. Optimise cross-selling and upselling

Intelligent cross-selling strategies can be developed by combining product and customer data.

• Customers who have purchased a camera automatically receive recommendations for suitable lenses or bags.
• Purchasers of sporting goods receive discounts for related products (e.g. running shoes + sports watches).
• Customers who have purchased a cheaper product variant can be targeted with special offers for higher-value models.

Why segmentation and data-driven marketing are essential

In a world where consumers are bombarded with countless advertising messages every day, generic marketing is no longer enough. Companies need to deliver relevant, personalised and contextual content in order to be successful.

Intelligent integration and use of data results in a variety of benefits:

• Increased efficiency: Marketing campaigns are more targeted and resource-efficient.
• Higher conversion rates: Customers receive precisely the offers that match their interests.
• Better customer loyalty: Personalised experiences increase satisfaction and loyalty.
• Automation and scalability: Dynamic segmentation enables automated adjustments and saves manual effort.
• Better inventory management: Marketing measures can be tailored to actual stock levels.

Segmentation and data-driven marketing only unfold their full effect when companies standardise their data, resolve identities and network systems. The combination of ERP, PIM and CRM creates intelligent, dynamic customer segments that can be addressed automatically with the right messages.

The result: companies are able to serve their customers more precisely, efficiently and effectively - and thus create a sustainable competitive advantage in an increasingly data-driven economy.

B2B2C transformation: from manufacturer to direct supplier through data integration

Traditionally, manufacturers rely on intermediaries to bring their products to the end customer. This structure has proven itself over decades, but it comes with some challenges: manufacturers have little direct contact with end customers, are heavily dependent on sales partners and often only receive delayed or incomplete information about market changes and customer needs.

However, by integrating customer data and utilising digital technologies, manufacturers can build a direct relationship with their end customers and thus tap into new potential. By linking their ERP, CRM and e-commerce systems, they gain valuable insights into consumers' purchasing behaviour, preferences and feedback. This information enables a faster response to market demands and the development of customer-centric strategies.

A practical example is Nike, which is reducing its dependence on retailers and increasingly focussing on direct-to-consumer (DTC) sales. By expanding its digital channels and integrating customer data, Nike has been able to create personalised experiences, develop targeted marketing measures and achieve closer customer loyalty. The company uses its Nike app and membership programmes to reach customers directly, provide exclusive offers and make tailored product suggestions.

With a better understanding of end customers, manufacturers can not only create personalised offers, but also offer additional services that strengthen customer loyalty and increase sales. For example, subscription models or customised product bundles enable a continuous customer relationship by offering packages and exclusive benefits tailored to individual needs.

Tesla is also making effective use of the B2B2C transformation: instead of relying on a traditional dealer network, the company sells its vehicles directly to customers. By analysing usage data from the connected vehicles, Tesla can continuously improve its services, provide software updates and offer personalised upgrades. This leads to an optimised customer experience and greater brand loyalty.

In addition, direct customer interaction opens up new product development opportunities for manufacturers. By analysing customer feedback and usage data, they can drive innovation in a targeted manner, adapt products more quickly and respond to new trends. For example, L'Oréal collects valuable information on customer preferences via its direct-to-consumer platforms and adapts its product formulas and marketing campaigns accordingly.

The B2B2C transformation is therefore more than just a sales strategy - it is a decisive competitive advantage in an increasingly data-driven economy. Companies that drive forward their data integration can not only operate more efficiently in the long term, but also build sustainable customer relationships and significantly strengthen their market position.

An integrated data ecosystem for the future

The integration of ERP, PIM and CRM systems as well as other tools such as BI (business intelligence) software creates a centralised data landscape that breaks down data silos and gives companies a holistic view. This opens up enormous potential for efficiency, customer satisfaction and competitiveness.

In a data-driven world, the ability to link systems together and utilise data intelligently is a key success factor. Companies that invest in integrated solutions early on will come out ahead in the long run - with better decisions, satisfying customer experiences and leaner processes.

By combining technologies, it is clear that the future belongs to companies that not only collect data, but seamlessly connect it to revolutionise their business strategies.

If you want to take your business to the next level and benefit from intelligent data integration, Arcmedia is the perfect partner. With extensive experience in linking ERP, CRM and PIM systems as well as customised digital solutions, Arcmedia helps you eliminate data silos and develop a future-proof strategy. Our experts will support you from analysis and implementation through to optimisation - for smooth, efficient and value-adding digitalisation of your company.

We have already prepared a checklist for successful system integration so that you can get started straight away. Use it to optimally plan and implement your integration strategy.

Checklist: Successful implementation of a data integration strategy

1. Define goals: Clearly define which goals are to be achieved with the data integration, e.g. increasing sales or improving customer satisfaction.
2. Identify data sources: Record all relevant data sources in the company, including ERP, PIM and CRM systems.
3. Select integration tools: Select suitable tools and platforms for data integration that match the company's specific requirements.
4. Ensure data quality: Implement measures to ensure data accuracy, consistency and timeliness.
5. Employee training: Train the team on how to use the new systems and processes to ensure effective utilisation.
6. Measure success: Regularly review progress against defined KPIs and adjust strategy as required.