Data requirements to get started with retail analytics
Want the full guide?
You might already be sold on the promises of retail analytics.
But is your organisation ready?
Do you have the answers to these three questions?
- What problems do I want to solve?
- What sort datasets do I have on hand?
- What is the general level of expertise of my team?
Think about what can be improved in the organisation.
- What if sales was 10x better?
- What if marketing was 10x better?
- What if product was 10x better?
- What if customer support was 10x better?
How do you measure each of those functions?
Let’s take customer support as an example. (commonly overlooked but it could make a HUGE difference)
--Start of Dialogue--
Problem to Metric
Why does this function exist?
Because it improves customer lifetime value. But it seems to be a lagging indicator.
How do we measure a leading indicator?
We think net promoter score (NPS) and customer satisfaction score (CSAT) could be a leading indicator.
How do we improve those?
We think average response time and average resolution time could be a contributing factor.
Can customer success directly influence those?
Data Sets Available
Are we measuring those metrics right now?
Yes. Manually. On Excel.
What do we need to better understand this function?
Well… it would be better if we could track a breakdown by
- Customer support representative
- Channel (e.g. email, text, website widget, social media private message)
- Type. of Problem (e.g. delivery delays, defective product)
- Peak time of responses
Are we collecting this data now?
Yes. But in 10 different locations.
Great. Better than nothing
Expertise on site
Do we have someone onsite to build these integrations?
Do we have someone onsite to run a regression analysis?
Is leadership willing to sponsor a new hire for this?
--End of Dialogue--
Once you start digging in, you will start realising that the underlying issue isn’t always the availability of the data.
Data set: Accessible & Clean
Even if you only have access to technology that may be a decade old, you should have a point-of-sale system that logs your sales transactions and merchandise information.
If you are an early adopter, you should have some of these in place
- Customer Relationship Management Software: Shopper
- In-store Sensors (Offline): In-store Traffic and Behaviour
- Inventory Management System: Product
- Google Analytics (Online): Website Traffic and Behaviour
Each one gives you a unique dataset which you can use for analytics.
There are two important points to highlight before moving forward. You have to ensure that the data is accessible and clean
Accessibility to Data
Regardless of the software vendor, you should have the ability to extract the raw files for your own work.
Take the point of sales system as an example, the data stored there might be required for accounting, inventory management and analytics.
In today’s cloud-enabled market, most software providers have and integration option or an open API that you can leverage to seamlessly integrate with the multiple tools that you will want to use.
If you have already committed to a vendor and they are not all that you thought they would be, change. Now.
Before it is too late. There are lots of cloud-based solutions that don’t cost more than $100 a month.
Cleanliness of Data
We have all heard phrases like “Data is the new oil”.
Oil is pretty useless unless it is refined.
An analytics platform is like a refinery that helps you make use of it. A refinery isn’t going to work as well if your data set isn’t as well maintained.
If the dates are all messed up or the products are mislabeled, the data is useless- clean it or trash it (depending on the state).
Word of Caution: Legacy vendors might not have kept up with technology best practices. Not all have proper database structures.
This will likely mean some shoddy patchwork to get a system up and running which can lead to complications down the line.