Improving Performance Analysis With External Data

Press enter to search
Close search
Open Menu

Improving Performance Analysis With External Data

By Mihir Kittur - 12/30/2016

Performance analysis is a vital tool for every effective manager. Managers in retail often find themselves asking questions like why did our market share decline, why did our conversions increase, or why did our margins move up? The answers to these questions can drive critical business decisions. However, undertaking good performance analysis is not a simple task.
There are several factors that to consider. First off is an inbuilt bias. As the ancient Greek orator, Demosthenes, said, “What a man wishes, he will believe”. Managers tend to look for proof of what they wish to be true, at the risk of overlooking that which is actually there.  Adding to this bias are lack of data, shortage of analysis skills, resources, lack of time, among other challenges. While some companies have made tremendous progress in overcoming some of these issues, many are still falling short of executing a holistic performance analysis. Part of the issue is that, even as retailers have improved their analysis methods, the paradigm itself has been changing.
The rise of the digital consumer and information transparency is at the center of this paradigm change. Consumers are constantly telling us what they like, don’t like, and how much they are willing to pay. They are leaving vital signals via their product searches, social media likes, product reviews and ratings, and more.

These signals are essential for getting a more complete view of any performance problem. Companies that tend to rely primarily on their transaction data run the risk of missing a very important understanding of what is causing their performance either to improve or deteriorate.
One client of ours was in this type of situation. Their market share had significantly declined in one specific product category, despite an increase in sales. The marketing team felt they had done what was required and the problem couldn’t lie with them. Similar sentiments were shared by the pricing and the merchandizing teams.

Each looked at the declining market share problem through their own lens, and within their own silo of transaction and market share data. They lacked an objective and complete view of the problem. The situation called for a holistic performance analysis to identify why their market share had declined. While it’s not an exact science, this type of analysis is effective at pointing our areas that might be playing a vital role in the decline.
The company put in place a more holistic approach of listing possible explanations and incorporating the rich consumer signals they’d never incorporated in the past. The questions they began to ask included: are there problems with traffic; is our pricing competitive; did the consumer have better product choices elsewhere; was inventory availability an issue; and could website user experience be better elsewhere?

To answer these questions, the retailer needed to gather external data on traffic, competitive pricing and assortment, out of stock occurrences, reviews, ratings, and social signals, and blend all of that together with internal transaction data and 3rd party market share data to ultimately uncover plausible reasons for market share decline.
This holistic performance analysis helped provide a more complete picture and enabled the marketing, pricing and merchandizing teams to uncover the true reasons for the decline and then helped guide more informed and impactful decisions. One of the explanations turned out to be that the client had replaced assortment in this category with private label products, and this was causing them to lose a lot of traffic to the national brand searches. Seems trivial, but it was having an impact.
Several such insights can result from this approach of blending newer sources of rich consumer data with transactional data. Performance analysis has gotten its own performance improvement - make sure you don’t miss out by ignoring external data.
Mihir Kittur, Co-founder and Chief Innovation Officer at Ugam Solutions.