“Data is the new currency” was the conclusion of the panelists-Vinod Bidarkoppa, Group Director, CIO & Board Member, Tesco HSC; Subhodip Bandyopadhyay, Director Organization, Systems & Supply Chain, Carrefour WC&C India Pvt. Ltd.; Ranjit Satyanath, Customer Care Associate & Senior General Manager, Solutions & Technology, Shoppers Stop Ltd.; Niraj Jaipuria, MD, BI Retail and Darrell Wisbey, Principal, Center of Excellence – Retail, ETP International Pte. Ltd. at ReTechCon 2013, who participated in the panel discussion on Business Intelligence in Retail.
Some interesting data points from the “State of the CIO survey – 2013” – that moderator Gunjan Trivedi, Executive Editor, IDG Media (CIO Magazine) used to set the scene:
- 74% of retail organizations still create / share insights over spreadsheets.
- Only 9% have moved onto mobility platforms.
While the above are in the context of developed markets like the US, its likely that the Indian picture would be no different.
Historically, most companies have used their ERP systems for generating analytics and MIS. When Business Intelligence (BI) tools were introduced, most retailers simply used the same metrics as in the past – they merely used BI tools to produce faster.
“The difference between ERP reporting and BI reporting is about converting information into actionable intelligence” was Niraj’s take – “But, not all retailers appreciated this and did not always have the support of good BI people to help them infuse BI into the DNA of the company”.
As an example of this, consider the difference between reactive vs proactive reporting: Most food retailers, have implemented an OTB (Open To Buy) system – i.e. the system automatically reorders on stockout of fast moving SKUs. A proactive, BI driven OTB system would be able to predict sales and move to replenishing on the basis of predictive analytics. Taken to its logical extent, the system would also be able to provide strategic reports of interest to the CEO & the Board – example – GMROII (Gross Margin Return on Inventory Invested) reporting.
“Implementing a BI system will clean your data by default. Data cleansing is like entropy – the need for it will ever-increase. A BI implementation is the equivalent of ‘spring cleaning’ – in that it gives you a fresh start.” – Ranjith
On the issue of how much BI can help create a “single version of truth” for key metrics – the view was that Businesses first need to get to internal agreement on what that “single version” is. For example – sales figures are presented differently in the financial system versus the merchandisers system (with the main differences being tax and discount accounting). BI frameworks can present different variants of this “single moment of truth” – across the company- but it needs agreement between Finance and Merchandising on how each of them will use and interpret the data.
“One of the fundamental building blocks to making BI happen – People – is expensive. “Reverse skilling someone from the business to become a Business Analytics engineer is a good idea. Over time, the investment in training pays off in terms of how the organizational DNA morphs”. – Vinod
“In Shopper’s stop – we have a team of people who regularly scan the data looking for patterns – for example – one of the patterns we look for is based on how customers complete a “look”. So we can find out all customers who purchased trousers and then send them a targeted mailer for shirts, ties, socks, kerchiefs etc.” was Ranjiths contribution to how businesses can use BI to create those “moments of truth”.
How do you compute ROI on BI?
The panel was unanimous that “you can’t”.
“It’s like trying to compute ROI on ad-spend” – said Niraj. “You can be fooled by spurts in metrics without having a full handle on all the underlying cause – effect relationships.”
“Rather than talking about ROI on BI – consider this question – how do you justify spend on BI?”. He continued. “If an investment in BI can bring your shrinkage down by 0.1% – that puts your breakeven at a few months. Similarly, if BI can help optimize inventory costs because of predictive analytics – then your breakeven is likely to be a few weeks.”
Wispey’s view was that “the biggest impact of BI is on availability. How do you measure a return on a customer’s feeling of “They always have what I want?”. Rather than run after a ROI metric – look at measures like Forward cover (for stable SKU’s) and a sell through rate (STR) for fashion or other fast moving items.”
“The ability to use BI is about BI is a function of business and promoter maturity” – was Subhodip’s view.
To the audience question on “How should BI treat unstructured data such as that from social media feeds?” – the panelists felt “Before tackling unstructured data – retailers first need to harness what they get from their ERP systems.” – this was Vinod’s view.
At the same time, there is a technical solution to the question. It involves using tools to “tag” common phrases and then analyse the grouping, frequency and intensity of the tags to arrive at conclusions. However, as both Niraj & Gunjan were quick to point out – “analyzing feeds from social media would cost you 2 to 3 times more – so there is a cost-benefit tradeoff to be considered”.
In conclusion, this is what the panelists had to say:
Wispey: “When I started in retail – I was taught a few simple rules – Know your customer better than they know themselves. In merchandise – it was right product, right place, right price. Retail has grown to the point where many of us have lost touch with the basics. While there is much data, the reality is that the world is now a smaller place – feedback travels and becomes an international incident before you know it”
Subhodeep – “set stakeholders expectations – define your single version of truth and invest in good resources who can sustain the project”
Niraj: “Build a project design which is scaleable. Retool the team to understand business drivers.”
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