Nailing targeted offers in the era of 'multichannel' retailing

This article is brought to you by Retail Technology Review: Nailing targeted offers in the era of 'multichannel' retailing.

By Rathin Das, Analytics Consultant at Mindtree Ltd.

A multichannel retail strategy is the future for businesses seeking to satisfy customers who increasingly want everything. Whether it is through digital channels, physical stores, direct mail or mobile apps, customers value parts of the shopping experience differently. However, a common thread is that they all like to be communicated to in a personalised manner.

Shoppers, who once relied on the advice of their local shopkeeper can now find products that meet their needs at a much better price. And, by using increasingly granular data, demographic and psychographic information, channel preferences, clickstreams on web, businesses are able to create highly "customised" offers that provide the right product at the right moment, at the right price and in the right channel. These are called "Targeted offers".

Companies can now pursue great value and enhanced competitiveness by using customer analytics and advanced data integration to drive "Targeted offers" programmes. In this article, we look at a framework to nail targeted offers by leveraging analytics. Companies may not be able to undertake all of them at once, but a "Crawl, Walk and Run "approach will certainly improve the offers.

Successful companies build "targeted offers" programmes through four broad steps as outlined in the diagram below:

 

Defining Objectives

The first question that marketers need to ask is, what do you want to achieve? Increased revenues, increased loyalty or new customers?

Consider Microsoft’s success with the e-mail offers for its search engine Bing. The objective of the targeted offer was to acquire new customers to try the service, download it to their smartphones, install the Bing search bar in their web browsers and induce them to make it their default search engine.

Retailer Tesco had a different strategy on its targeted offers, their aim was increasing sales to regular customers and enhancing loyalty. Therefeore, by studying the usage patterns of the customers by tracking the Clubcard, Tesco provides localisation by adjusting merchandise offers at an individual level across formats, from hypermarkets to local stores. This has led to redemption rates significantly above industry standards. So, starting with a clear objective is essential along with having a flexible approach to fine tune offers based on changing business landscape.

Integrating Data across Disparate Sources

“Targeted offers” based on customer insights will most likely lead to improved response accuracy. But marketers face steep challenges, in this cluttered digital landscape, to gather and integrate data which will provide vital customer information, purchase behaviour and product offerings.

Understanding customer needs, their life cycle stage and purchase context will enable businesses to map the product to these attributes which is vital in order to craft targeted offers. Basic information like age, gender, household size, address, income, lifestyle and behavioural data can be easily acquired or derived with help of loyalty programmes.

However, the growing availability of SoMoLo (Social, Mobile and Location) information adds to the complexity of data mining. Customer profiling based on the demographic and behavioral information, along with added dimensions of SoMoLo and preferences, enhances customer intelligence for the marketers.  Marketers are beginning to craft offers based on where a customer is at that given point of time, and what his social interests are from his media posts.  Walmart, for example, has acquired a social media firm to leverage SoMoLo data for its offers. They can now predicting purchases by shoppers at Walmart.com based their social media interests and even geospatial / location data in real time is being leveraged to estimate customers’ age, travel style, level of wealth and the next likely location.

Knowing the products/services helps marketers create a classification system of their products and services. The attributes are narrowed down to sub-brand, product type, colour, form, size, price, composition/material, volume to finally SKU number. These help customers optimise their search online and offline as well as helping marketers to tag the best targeted offer to that attribute for an individual.

Analysing and Implementing Treatment Strategies

Once the companies have systematically gathered information about the customers, product attributes and purchase contexts, they need to extract actionable insights out of this. There are proven statistical analyses and predictive models that can help marketers derive an enormous amount of consumer insights from this raw data. Analytics techniques not only help in understanding patterns but also provide insight into say a customer’s likelihood to respond to a retailers discounted cross sell offer delivered on a mobile device.

Customer profiling and behavioral segmentation also helps identify similar groups of consumers by simultaneously accounting for customer demographics, attitudes, purchase patterns, history and preferences. Marketers and retailers can now be aware of who are the most profitable customer group and which group are most likely to defect. Business rules can then determine the customised offers, or whether the contact frequency be limited, in order to avoid the risk of reduced response rates due to high contact frequency.

Implementing the treatment strategy is the key to a successful “targeted offer”. Often the channel of contact is considered the best channel. However, there are occasions when inbound and outbound channels should differ. For example, a leading bank studied the campaign responses and learnt that mortgage offers presented through an ATM at the moment of customer contact did not work as the customer did not have time for the complex offers whereas they might be receptive when they walk-in to a retail branch.

Learning and Evolving

Finally, measurement and accountability enables marketers and retailers to evaluate the performance of their offers and create business rules for the next offers. These rules should be driven by data analysis and metrics tied to the objectives. Some trends in implementing premium offers are made in a one-on-one interaction, a reward for customer loyalty on responding to offer on a new category that a customer has not purchased before for example.

The key is that retailers and marketers must work together to roll out a fully integrated, optimised, multi-channel “targeted offer” strategy.  By working together you can incorporate customer, product and purchase context dimensions in the  “Targeted offers” strategy and then by  implementing cross-channel data collection best practices and seamlessly integrating consumer information you can avoid the pitfalls of siloed communications.

About the author

Rathin Das is Analytics Consultant at Mindtree. Rathin has significant experience in marketing analytics, research and category management driving consumer and shopper Insights in the retail, consumer goods, media and travel industries.

He carries extensive experience in retail category management having worked with India’s largest retailer performing demand aggregation and planning, promotion planning and vendor management