Prepared for Lloyds
ROI: return of $308K per annum (6X ROI) for a group of 10 merchants, with opportunity to scale this across 1000+ merchants.
An easy-to-use, business-facing analytics tool that allows a bank’s merchant customers to explore their sales data and obtain actionable insights - unlocking the value of the data held by their bank.
Problems we were tasked to solve and audience
Merchants and SME businesses currently don’t have access to useful data analytics. While sales data is available via their point of sale system - they are not able to compare their results across industry, see demographic information or quickly see trends and insights across their business. In addition, this sales information is often difficult to access for people in functions such as marketing and operations - and is usually available to the owner or finance leads.
This large Australian bank saw the opportunity to allow their merchants access to transaction and demographic information to help them make better business decisions. By allowing this data to be accessible to merchants, it created brand loyalty and meant their bankers were able to understand their merchant clients in more detail - and have meaningful conversations with them about value-add products and services that could be added.
Frequently used terms:
Result delivered (sample questions)
Summary of value
Return of $308K per annum (6X ROI) for a group of 10 merchants, with opportunity to scale this across 1000+ merchants.
Prior to Hyper Anna, the bankers would spend about 1 week (40 hours) every quarter to prepare sales and customer insights for their merchant customers. With Anna, the preparation time went down to 30 minutes as the need to manipulate multiple systems and complex excel files was removed. Bankers would only need to ask a few questions in Anna in order to prepare meaningful conversations with their clients. This resulted in 308k being saved by the bank. Moreover, merchants were equipped with the ability to self-serve insights in Anna, shortening the process from having to ask questions to their bankers.
Summary of value
Every merchants who used Anna said it showed them interesting insights about their business - especially in the areas of sales performance, benchmarking and demographics.
It was used to influence decisions such as which promotions to discontinue, how to tailor marketing campaigns for their high-value individuals and which locations may be ripe for expansion.
Some key quotes from merchants include
“It's not a 'want,' it's a 'need' - it's a highly digital age, it's something that's expected from a banking provider”
“It's very hard for us to track this information per venue or location. Having one place where it's all available in a very user-friendly location is very valuable.”
“What I liked about Hyper Anna was the geographical information she could tell us about our customers - not only where our high-value customers live but also who is a local shopper and who are on holiday or live outside our scope. This gives a better understanding of where to put our outdoor media campaigns, store events, VIP day marketing etc.”
“These data points flow into marketing and campaigns”
“It was great to see whether or not to continue a lease or expand a store.”
Tasks such as creating your own reports or dashboards require the knowledge of where to navigate through your data for insights, but this has been proven difficult and time consuming.
Using the power of AI, Hyper Anna executes millions of hypotheses to discover complex patterns in datasets. The most practical and relevant insights are then pro-actively delivered to your inbox. In contrast to large teams of analysts finding answers from data, Hyper Anna will single- handily do it in a matter of hours.
In this example below, Hyper Anna automatically suggests a manager of a burger franchise to look at where customer spend has significantly over- or under-performed, and importantly, the factors that have contributed to this.
Getting insights from data is now as simple as asking a question.
In this example below, a manager has asked Anna to identify where high spending (value) customers live.