In part 1 of our blog, we asked if there is a way for banks to improve on their “special offers exclusively for you” as we approach the holiday season. In part 2 of our blog, we look at how banks can do this.
Bringing it together with precision marketing
Precision Marketing brings to the picture the all the dimensions that are currently missing in most approaches taken by banks in reaching their customers in such situations. For example, banks can provide location-based offers, but these are not tuned to the individual’s preferences. Or, the bank knows the preferences of the customer, but doesn’t know how to reach them when it counts. Precision marketing prioritizes offer relevance by considering the following questions when suggesting offers: Where is the customer currently located? What is the customer profile, shared preferences and shopping interests and past buying behaviors, and what time of the day is it? Optionally, what has the customer indicated about their shopping plans?
And based on this – it answers with the most relevant and prioritized merchant offers that make sense to recommend in accordance with time, location, profile, preference, buying behavior, etc. All delivered to the customer via their mobile or other device of choice. The beauty of precision marketing is that it learns from the customer’s actions (or non-reaction) in response to an offer, further refining its predictive capabilities when making recommendations the next time around. Overlaying this big data technology with accessible analytics also enables merchants to monitor offer effectiveness, allow for fine-tuning of offers and ROI measurement of their participation in the program.
There are winners in every corner for such a solution:
Retail bank customers win because they can access the most relevant savings and discount offers – they are prompted with offers at the relevant time, in real-time
Merchants (also customers of the bank) win through increased traffic to their physical and virtual shop-fronts, and increased brand loyalty
The bank wins as they can help drive spending on their consumer credit products, increase customer loyalty, as well as comply with privacy laws as customers opt-in to the program (and other marketing initiatives).
The art of the possible
Such an initiative could be extended with additional features – what if an offer could be priced based on a customer’s relationship with the bank, or additional loyalty discounts made available on the fly for priority customers? What if a customer could see what their social circles (family, friends, gym-buddies, etc) or peer groups are spending on, or what offers they are taking up? Imagine being able to ‘wish list’ products by scanning barcodes and having merchants bid to make an offer to you. In this case, the bank could also collate such wish list data to understand demand, and suggest to merchants certain offers that would be interesting for customers.
Gamification elements could be added such as broadcasting of an offer take-up by a customer on social media, allowing customers to switch between ‘modes’ (e.g. [offer]-Hunting mode), earning credits for recommending offers to a friend, or even real-time trending (“People with your profile and within 1km of your current location claimed these offers in the last 24 hours”). Social responsibility elements too can be incorporated – “for every offer redeemed, the bank will donate 20 cents to a charity of your choice”. If banks cast their minds wider, there could be many other applications to which this concept can be extended and the typical bank loyalty programs transformed.
How can banks begin on this journey?
Banks can start the journey by deploying a real –time offer solution that leverages existing customer information in combination with information provided by a mobile device as to the customer’s current location. The real-time offer solution should be self-learning and gradually improve the relevance of its offers. This same application should allow the customer to opt-in and indicate what she/he is currently looking for.
Gradually the bank can build up more detailed information on individual customer’s preferences, buying habits and transactions to be more on-point with every new advice or recommendation. This way the bank can also gain a better understanding of the different customer-groups that it is trying to serve, especially its Priority Banking customers.
Further refinement is possible by leveraging information from social media. Making sense of these big data volumes to discover trends earlier than the competition requires predictive analysis capabilities. And gamification would bring in a fun element that can increase the uptake of the bank’s offers.
Obviously all of this only works when executed in real-time, which is now more or less routine with in-memory technology.
What about costs?
Traditionally such a program would come with high upfront capital investments and lengthy implementations. But with private cloud as a maturing option, systems are up and running in weeks and costs change from capital expense in year one to operational expense over many years. So now that technology is no longer a bottleneck it is up to the banks to prove that they take their customers seriously.
For our readers, how well is your bank is doing in the loyalty program stakes, which banks are getting it right, and what other attractive features would you like to see included?
For bank customers out there: if you were to design your own bank loyalty program from scratch – what would it look like?
Brenda Ng specializes in Customer Experience for Financial Services and is part of the APJ Industry Value Engineering Group.
Louis Teunissen is an architect with the APJ Industry Value Engineering Group specializing in Banking, Insurance, Leasing and Cloud