Mitre10 had volumes of data and no trouble accessing it but no easy way to visualize and analyze it fast.
“But when it comes to literally hundreds of millions of rows and potentially literally billions of rows that you need to process quickly the infrastructure you need for that is very different and that’s where 11Ants in my opinion has been very powerful for what I’ve been trying to achieve.“
Watch a video of how 11Ants enables Mitre10 to dig deeper into their data and to truly understand their customers.
Key Business Benefits
Large volumes of loyalty data became possible to analyse quickly
Ability to drill down into retail loyalty data to find out what is really happening
Ability to understand transactional patterns easily
Speed to implementation
Mitre10 Video Transcript
Mitre 10 is a DIY home improvement retailer in New Zealand with around about 84 Stores.
Data today is becoming as important as having products in your store and therefore the more understanding you can have about your business and about your customers through the usage of data the greater the competitive advantage you have in the marketplace.
The key in retail, to being successful, is to understand your customers as much as possible but the reality is that the volume of data we have today and the speed at which we have to analyse it and make decisions around it goes far beyond typical approaches to data.
Often it’s not so much there being a problem with getting the data – it would be primarily to do with accessing it.
Over the last five to 10 years there’s been a real explosion in what we call data visualization tools. The ability to take millions of rows of data and then to visualize it and then to drill through it. But when it comes to literally hundreds of millions of rows and potentially literally billions of rows that you need to process quickly the infrastructure you need for that is very different and that’s where 11Ants in my opinion has been very powerful for what I’ve been trying to achieve.
So with 11Ants what it allows you to do is it allows you to range from a business view of what the data is doing at a very aggregated level. What’s the department doing let’s say, all the way down to actually, I’m interested in this store, that brand and this type of customer.
So having a tool whereby you can sit there, ask yourself a question, work through the different logics around how to actually get that information and then let it run and do that over 104 weeks of transactional information down to an individual customer that’s many many hundreds of millions of rows of data. That’s a very very powerful tool.
It’s very useful for understanding transactional patterns. What brands are selling with what other brands? It’s very good for looking at promotional performance over varied time frames and different levels of granularity. So I can see whether or not a certain promotion is driving increased basket spend or more customers coming into the store. Now the great thing about it is I can do it at multiple different levels. I can decide to look at a brand very quickly and see whether or not a brand’s performance over the last six months is improving because more people are buying into it or the people who bought it, or actually buy more of it. I can then change that and go down to a product level or fineline level.
Rather than just working in an aggregated world that we often have to work within e.g., My sales this week were? This department did X? Year to date is Y? It is easy to actually to go. Something’s happening over here in this group of stores. Why is it happening? Okay let’s drill into those stores. Alright in those stores let’s exclude this department and this department. Okay we only want to look at these brands.
The great thing about 11Ants is that it works from the very granular data and works up which means you often keep all of the insight that you might have that might be relevant to your decision.
The key selling point for 11Ants and is the speed at which it can get up and running and how that compares to some much larger implementations. So a previous example of working with 11Ants was we provided, I believe it was around about over 100 million rows of data, provided I think first in the morning. We were discussing it online in the afternoon! That’s quite a powerful ability. It gets us to think about the customer more than just think about that product sold that much at that margin was that waste was that. It actually allows us to have a little look into the basket and asking okay well why are these things being bought together. What are the demands that our customers are looking for?
If you’re a business at the moment who doesn’t understand your transactional information. If you’re a business today that’s spends all your time working at the level of aggregation where you have more questions after the meeting than you have on discussions around what’s really happening. You need to be thinking in this space. I would absolutely recommend 11Ants.
DIY Case Study
Mitre 10 is New Zealand’s largest home improvement and garden retailer and with a growing market share and store count. While the retailer could access its huge amount of data – it had no easy way to visualize and analyze it fast.