How to promote omnichannel?

The objective of the ominichannel analysis is to be able to follow the consumer throughout customer experience via different consumption channels.

Omnichannel: a change in behavior linked to COVID

The year 2020 was affected by the COVID epidemic that we all experienced. In this context, many customers have changed their consumption behaviors: change of brands, change of purchase frequency, change of consumed products and average basket amount… Many are the evolutions and adjustments that have marked consumers during the year. Among these changes, we have also seen a shift in consumption channels, in particular in favor of digital channels and omnichannel.

If in the context, this evolution has been more undergone than provoked by the brands, it turns out that a large majority of brands are looking to increase the omnichannel consumption of their customers. Numerous customer knowledge tools allow them to find drivers to encourage multi-channel consumption.

Identify the consumer typology 

First of all, setting up a comparative profile of the different types of consumers (exclusive to stores, exclusive to e-commerce, mixed for example) will allow us to understand the particularities of each group and to identify the drivers to act on. For example, let’s imagine that a product is over-consumed by “store-exclusive” customers, customers that we would like to see become “mixed” customers and therefore also buy on the e-commerce site, we could then make a specific offer on the product in question for any purchase made on the site. On the same mechanism, we could also imagine a special “click and collect” offer for e-commerce customers that we would like to bring to the store (provided, of course, that a store is located near their place of life).

Sharpen your strategy with predictive models

To go further, predictive models, and in particular the channel score, will allow you to anticipate natural changes in behavior and thus sharpen your strategy to influence consumer behavior. For example, if we set up a purchase intention score on the e-commerce site among “store-only” customers, we will be able to predict the probability that a given customer will buy on the e-commerce site in the coming weeks. Based on this information, we can then differentiate the animation strategy into different groups. For example: 

• Customers with a very high probability of migrating will not benefit from any particular animation to encourage their migration
• Those with a medium probability will benefit from promotional offers to encourage and facilitate this migration
• Finally, those with a very low probability will continue to be animated on their preferred channel in order to avoid an undesired drop in sales

How to promote omnichannel with datacadabra?

Within datacadabra, many methods are available to work on these issues. In particular, the Describe module will allow you to work on customer profiles and compare the consumption behaviors of different groups. Within the Predict module, the different scoring models will allow us to anticipate future consumer behavior. Our different analysis methods are very simple to implement and allow you to build your action plans with ease.

Want to know more? Do not hesitate to contact us or to ask for a demo of datacadabra.