Product range design

What is the challenge?

The management of all offers of a company – the product range – includes processes between manufacturer and retailer. Offers are strategically grouped with customers in mind and presented for sale. For companies, product range management also means considering design strategies for sales promotion, new products and sales prices. For an optimal management of the assortment, a lot of information has to be taken into account, the collection and processing of which is complex and time-consuming in a manual way.

What data can help?

  • Customer data (socio-demographic data of the target group such as value of usual shopping baskets, income and lifestyles, usage behavior, data from customer profiles, contract data)
  • Company data (on assortment and characteristics, availability of goods)
  • Market data (historical data on demand and sales of comparable products, competitor assortments)
  • IIoT data (Industrial Internet of Things)
© Shutterstock
Use case category: range design

How can companies use their data?

Data can help shape the product range in a way that is optimal for customers, enhancing the buying experience and revenue streams. Data analytics help assemble product groups based on customer search behavior and needs (e.g. optimized assortment and supplier selection and procurement when a specific supplier preference has been identified). An important aspect can also be the dovetailing of assortments in offline and online business. The analysis of product and market data determines the optimal composition of the assortment from different articles and avoids the supply of unprofitable articles, inventory gaps and overstocks. The benefit of data-based product range design therefore lies in creating transparency about which product groups have the best sales potential in which combination and what impact changes in placement, price and combination can have.

Where is this use of data already being applied?

Data analytics help the Real retail chain to highlight previously unknown sales hits. This allows products with higher sales, so-called top sellers, to be better identified and the assortment to be geared to them.

The start-up Busnetworx manages trip details such as start, end, vehicle type and number, intermediate points, idle times, assignment data, traffic data for bus companies. Based on this, preference, profiles can be created and the offer optimized.

The consumer good company Procter & Gamble collects current data on the usage behavior of its customers via IoT devices. This information is then used to better adapt its own product range to customer preferences.

The media company Netflix collects data on the usage behavior of its customers. The aim is to estimate how successful a film will be and to adapt its own range accordingly. For example, the rights to the series House of Cards were acquired on the basis of a data analysis. Among other things, the high number of Netflix users accessing the film The Social Network influenced this decision. David Fincher directed both offerings. At the same time, films starring Kevin Spacey, who also starred in House of Cards, saw high hits.

How does this use of data contribute to value creation?

Value creation can be strengthened by optimizing the product range. For example, it is possible to improve merchandise group profit, direct product profitability in placement, inventory management and sales promotion campaigns. It is also possible to use the findings to design new products, thereby generating additional cash flow.

Aim of data use

Sources: real GmbH (2016), Busnetworx (2018), Procter & Gamble (2019), Netflix (2019), Neil Patel (2019)