Leveraging our unique, robust integration with global providers of enhanced transaction data and comprehensive data-science capabilities, which were refined against billions of transactions, Tapwise can be a game-changer for businesses.
This critical consumer data and enrichment empowers our customers to learn about their audience quickly and effortlessly. And the massive scale of information allows the data intelligence of Tapwise to achieve strong pattern recognition and prediction, allowing forecasts to be used to give customised and realistic marketing guidance, giving our customers the tools they need to outperform the competition.
Through an integrated system of direct data access and HTML encoding, Tapwise offers profound financial data for merchants covering numerous different industries, from personal finance, shopping, dining, to investing and beyond. Besides, each data source goes through a 9-step process to boost data with merchant recognition, classification, geographic location and reconciliation for 100% accuracy.
As they are consolidated, a significant and essential aspect of the data aggregation process is defining the merchant, clarifying the transaction, and sorting transactions into categories. The machine learning and data science algorithms deliver the most reliable classifications and simple, descriptive, and easy-to-use transaction data, standardised across every data source.
The audience tab shows extensive analytics about your current audience.The followings are some of the KPIs you can refer to run rewards programs to keep your legendary clients engaged, and convert more of those from the bottom of the pyramid into VIPS.
We calculate the average spend of your customers and compare it against the average of all your competitors in our database over the last week, month, quarter and year.
This number indicates the average number of times a single customer buys from you over a week, month, quarter and year; depending on what makes sense for your business. We compare this value to the average of your competitors.
The total number of clients you have. This tab also shows how the audience is growing or shrinking over time.
This tab shows the number of clients who are at risk of leaving you based on their purchasing behaviour over the last quarter. We may find that they started buying from your competitors or simply stopped buying from you.
We allow merchants to run a specific campaign to target these people and ask them to come back in exchange for something special, which is a perfect example of how Tapwise can help.
This classic and straightforward metric compares quantitatively new and returning customers. It also shows how each group is growing or shrinking over time.
The retention rate is your ability to retain customers.We calculate your retention rate with this formula:
RR = ((EC-NC)/SC)*100, where:
EC – number of customers at the end of a periodNC – number of new customers during that periodSC – number of customers at the start of that period
The operating score is a value on a scale from 1 to 10 that gives the merchant an idea of how they are doing against the best performing competitor in the market.Please note that we do not reveal who the best performing competitor is.
To calculate the OS, we use the following formula:
First, we multiply your ATV by your ANTC and weigh the result against your retention rate (RR); which, again, is the ability you have to retain your customers.
Then, we need to “normalise” the result to be able to compare apples to apples. In other words, we weigh you against your competitors.
The formula looks like this:
(Your “denormalised” OS – The lowest value amongst your competitors)/(The highest value amongst your competitors – The lowest value amongst your competitors)
To simplify, OS = (You – Xmin)/(Xmax – Xmin)
By definition, normalising your data gives a value between 0 and 1. As we decided we want it on a scale from 0 to 10, we only need to multiply the result by 10.
A bar chart illustrating what postcode and suburbs your clients live in.
A bar chart illustrating what postcode and suburbs your clients like to shop the most, based on real transactions.
A bar chart that shows what other industries your clients like to shop most often, based on real transactions.
A bar chart that shows what other stores your clients prefer shopping from, based on real transactions.This information could be precious for creating cross-branding promotions, i.e. “spend $100 this weekend with us, and receive 10% cashback on your next purchase at Rebel.”
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