Mobile Apps from Consumers: Gold Mine for Sales Forecasting

When we talk about Big Data in Retail, we usually think of collecting sales data to gain a better knowledge of consumer buying behaviours.  resulting adaptations linked to the supply chain (products, suppliers, outlets).

 Now with the multiplication of mobile applications, or apps, we’ve seen a dramatic emphasis on the consumer experience. This generates and collects new categories of data, which is useful for refining sales forecasts and replenishing distribution networks.


Mobile Apps Serving the Shopping Experience

As noted by Forrester Consulting in the “2016 Mobile and App Marketing Trends” report, mobile phones and apps are a major channel for customer relations and shopping. This is a growing role, especially with the development of connected objects, AI and virtual reality (VR).

As a central link in this (r) evolution, mobile apps are opening doors to innovative and inciting possibilities for consumers. Brands are now equipped to tend to customers directly by offering them more personalized services than ever before. Anything that can improve the shopping experience also helps improve Retailer knowledge and selling more.

The universe of mobile applications for consumers is growing steadily. One can distinguish the applications as brand-specific, distribution brands and shopping centres. Additionally, there are those created by third parties (often start ups) also offering new services.

 In this second category, for example, mobile applications designed to facilitate in-store clothing purchases, including those that enable the right size to be purchased without trying it on. These services use the user’s morphological data (previously stored in the application by the user) to define the corresponding size for each partner brand. No need to go into the fitting room when buying from a store and no need to order three different sizes when buying online.

New Services = New Customer Data

Beyond the fact that a mobile application results in an accelerated purchase (in-store) and a reduction in product returns to be managed (online), retailers experience other advantages. Those advantages fall primarily in terms of customer knowledge since new service brings new data about the buying behaviours for analysis.

Morphological data can improve behavioural knowledge and provide a more realistic view of the consumer. Some illustrations include:

  • Anticipation of specific client profile expectations, such as pregnant women.
  • They can promote a better understanding of seasonal specificities, for example during the traditional renewal of the wardrobes of children and adolescents (smaller sizes) at the beginning of the school year.
  • They can help to better analyse local specificities. For example, for sizes (S, M, L, XL) mostly bought by region.

These are only a few examples and mobile applications that provide innovative services to consumers rapidly. Technology constantly nourishes this trend. Examples of apps enable things like virtually trying makeup and help to choose a shade according to the skin complexion or desired style. There are also community apps which allow users to share and record their looks. Those enable posting the photo of a garment spotted on a passer-by in the street in order to find it at a store nearby.

Tremendous resources of new data are being created and they serve as valuable indicators of purchasing behaviour and trends. This data is used by retailers to optimize demand forecasts and assortments of reference styles in their store networks.

Mobile applications have become one of the main vectors of this data and are likely to become the first medium-term source. This is not only because almost everyone has a smartphone but because the services offered by these mobile applications generate information that is even more complete, precise and rich.

It remains to be able to exploit, collect and analyse the data. Acquiring more data is a good thing but having the capacity to process them is just as important. Some of the latest generation technologies – starting with AI and more specifically Machine Learning– are making great progress and providing decision support of significant value as Big Data continues to grow in volume and diversity.