In November 2014 we launched cashback offers to Meniga’s personal finance management (PFM) customers in Iceland. Read on to find out how we developed these offers and our learnings so far.
Meniga’s PFM enables Icelandic consumers to have insight into their spending and financial management. In other countries, we are whitelabelled through banks, but we have made the tool free of charge for people in Iceland since 2009. This local community provides valuable insights for our development team, giving us exceptional insight into real people’s money management needs.
In short the results since the launch of cashback offers have been astounding. On average 5–10% of those that see an offer choose to make a purchase and some of our users have been receiving up to +€1.000 in cashback on offers provided to them by the system.
Also, only 2% of those that have seen offers have elected to opt-out of the cashback offers program.
A survey we did prior to the launch told us that while most of Meniga’s users (64%) were happy to receive relevant offers, based on their spending data others were indifferent (17%) or not all that excited about the idea (19%).
Some resistance is understandable: general privacy concerns, Big Brother tendencies of software companies and marketing banner fatigue all contribute to people on the fence or suspicious of data-informed marketing.
With these reservations in mind we took a number of steps to ensure that the experience of receiving offers was both inviting and transparent to the user.
The first step was to be transparent. We explicitly told the user why he or she was receiving each offer, and that the merchants would not receive any personal or financial information about specific users in the offers they were providing.
The second step was to blend offers into the user experience of the PFM. The offers were designed to have a consistent look and feel, appearing in the PFM’s feed as other interesting user events and transactions.
The third step was to create a rationale for why the offer mattered.
To do that we built an engine to forecast how much the user would potentially save by using the particular offer. The forecast was based on the user’s past spending and forecasted budgeting in the designated PFM category of the offer. Since we also knew that it would be unreasonable to suggest users use the offer’s merchant solely for the remainder of the offer, we weighted in the popularity of the particular merchant with its current customer base in relation to other merchants in the same category.
Multiple relevance hooks can be added to an offer such as:
Similar spending hook
The similar spending hook informs the user how much spending he or she has done at similar merchants to the one providing the offer. The user can click a link to reveal the lists of similar merchants and make a quick ballpark estimate on how much utility he or she will have from the offer.
The interest hook will inform the user how many other users who have received the same offer have also activated the offer and therefore find it relevant.
Cashback earned hook
A cashback earned hook will inform the user how much cashback on average other users have been earning. This will tell the user how useful other similar users have found the offer to be and potentially what the user might expect to save on it.
Others hooks are also available, and for each offer, different and multiple hooks can be configured. Likewise, the first relevance hook we created, the estimated savings hook that is currently used with Offers, can also be shown.
With relevance hooks we think we have taken the next step towards creating more useful data-informed spending motivators that help our users make better and more informed decisions on why the offers they receive matter.
This fall will see the release of the new Offers UI in Iceland accompanied by the new set of relevance hooks. I will be keeping you updated on how relevant our users actually find them…