How MDM Enables Innovation

Innovation always occurs when existing offerings are further developed and rethought. On the one hand, this can affect the product itself, but also the way it is presented or made available. Since these developments always aim to make the offer more attractive to the target group, customer needs are always the starting point for every innovation. But as Steve Jobs already knew, this is a big challenge, because customers themselves often don't know what they even want – until you show it to them.

Customers Understand...

In order to find out which offer triggers people the desire to absolutely want the product or service, companies must better understand their target market. Just as it is always easier to give gifts to people who are close to you and who share their dreams and interests with you, every piece of information about potential customers is a valuable piece of the puzzle from which an ever more accurate picture is formed. And since Steve Jobs is right and customers don't know what they want, it's not enough to ask them – companies need to show it to them. And to get it right with as many people as possible, they need tons of data. They need information about what existing customers had already bought in the past, what they were interested in, what they returned, and why. You need to know exactly whether there was contact with customer service and what was the reason for it. It is important to know whether and what customers have said about the brand, the product, the service on platforms and on social media. And it's important to know how they became aware of the product – which advertisement, which link they clicked on to get to the offer.

In this way, the image of “the customer” is gradually being formed, which makes it possible for companies to segment their target audience in a meaningful way and thus address them even more accurately. For example, if a company produces high-quality pans, it can make sense to develop various campaigns, each aimed at different personas. For example, professionally very busy people who attach great importance to lifestyle and uncomplicated dishes could be more likely to start a campaign that shows the evening cooking ritual as ideal relaxation after a stressful day at work. Perhaps an offer with a cookbook full of healthy and easy-to-prepare recipes could be a suitable incentive for this audience to positively influence the purchase decision. For another target group, for whom cooking is primarily associated with family and necessity, a different product presentation certainly makes more sense. Here, you would perhaps rather emphasize that the pan is very easy to clean and has a long lifespan. Cross-selling offers with discounted cooking appliance sets may be more suitable for picking up customers for their needs.

... and Anticipate Needs

Based on past marketing activities, it is also possible to make predictions about the results planned campaigns are likely to lead to. Here too, the more data is collected and evaluated, the more precisely companies can predict how successful communication measures as well as product innovations or other offerings will be.

The Basis? Master Data Management!

Both in order to understand how the target market thinks and to make predictions about how successful certain measures will be, companies therefore need data – of various types of product data as well as customer data, but location data, supplier data and even competition data are also important additions. Because the more extensive the information situation is, the more context can be created and the more valuable insights can be drawn.

Master Data Management This is where it starts: It uses various data sources such as ERP, PIM, PXM, PLM, CRM, CDM and many more to create a consolidated data situation and make it available centrally. MDM uses business rules to further process and transform this integrated data into knowledge – knowledge that ultimately enables companies to act.

The Role of Analytics and Artificial Intelligence

MDM's ability to draw knowledge from data is based on a range of analytics features. Descriptive analytics evaluates historical data, while predictive analytics uses these findings to predict results based on new parameters. Prescriptive analytics goes one step further: Based on the forecasts, recommendations for action are generated which, for example, can positively influence the probability of success.

All of these tasks related to product development, marketing and sales strategy are extremely important in order to build a sustainable business. For employees to be able to address such sustainable issues at all, the technology used must be designed in such a way that as little manual intervention as possible is required. Therefore, most have MDM technologies about AI functions that perform many repetitive tasks. This includes, for example, automatically tagging images or finding data anomalies to secure the data quality. There are many other examples of the use of AI to make it possible to handle mass data effectively in the first place.