In military strategy, communications, command, control and intelligence (C3I) technologies are considered to be “force multipliers” — that is, they dramatically increase the effectiveness of a given number of troops or weapons, enabling them to achieve results disproportionate to their size.
An example of this would be an Airborne Early Warning System, which watches over a large swathe of territory, enabling ground forces to conduct pinpointed operations, thereby improving their effectiveness. In much the same way, data analytics are the modern-day marketer’s force multiplier, helping to improve RoI (return on investment) of marketing spends manifold.
Through data analytics, marketers can build profiles of consumers’ interests and shopping behaviours and these can be used to predict their needs and target them via specific marketing communication on channels that they are most likely to access. This increases the efficiency of marketing spend and delivers a much higher RoI than traditional broadcast messaging.
In order to activate data effectively, marketing organisations need to go through four steps:
• Build a customer data foundation
The first step would be to create a customer data platform (CDP), which compiles as much of data on the customer as possible, from all parts of the organisation. Clustering algorithms can then be used on the data set, to create segments of consumers that display similar behaviour/interests.
Over time, the data can be overlaid with other signals left behind by consumers — site visits, products purchase, response to promotions etc — to create a more holistic profile of the customer.
• Mine the data to identify relevant triggers
Once the CDP is set up, sophisticated data mining algorithms can then be used to identify the decision criteria or rules, that would trigger action by the system. These triggers could be events like “browsed but not bought” or “received coupon but no action taken”, etc. With machine learning, the accuracy and complexity of decision criteria can be incrementally improved, enabling targeting of very specific segments at appropriate moments.
• Design the right offers and messages
Understanding customers and how to engage them is only half the battle, the next step is to design messages that are relevant to them. Once a trigger is activated the message created should result in the desired action — for example, an “abandoned basket” trigger could activate a coupon that can be redeemed only within the next 48 hours on items that were already there in the basket.
Similarly, online purchase of gym equipment could also trigger offers on exercise wear and sports shoes.
• Deliver the messages via the appropriate channels
Data analytics also enable a better understanding of the consumer-channel interaction. For example, an automotive company that has been spending heavily on TV advertising might realise that potential customers spend a lot of time browsing on auto brand and dealer websites. In that case, improving their website experience and advertising online might deliver higher returns than traditional advertising on TV.
With the right use of data analytics, marketers can turn their communication channels from blunt instruments into specific and directed tools that deliver targeted messages to the right audiences, thereby multiplying their marketing return on investment.
— The writer is a Director at Kantar MENAP
