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Adopting a Data Mindset in a Retail Organisation

May 27th, 2014

Author

Content Team

Categories

Sales Forecasting

Words

3900

Estimated Time

16 Min

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[ This article is inspired by the commentary from a thread composed by members of the Modern Sales Pros community — an exclusive, invite-only, group of sales operations, enablement, management, and leadership experts. We are sponsoring membership for any of our readers who want to join the community — apply for community membership here and put us “MoData” down as your sponsor. ]


Photo by Josh Hallet
The article explains how to adopt a data mindset – one of the most critical management challenges facing online retailers today.

1. What is a ‘data mindset’?
2. The data champion
3. Get more data, give more data
4. Data for continuous improvement

What is a data mindset?

When an organisation has a data mindset, every single person working there, from the CEO to the cleaner, uses data to inform their decisions. Agreement is required for when data should not be shared, rather than when it should. Access is easy and fast, with no need to go through IT departments and write SQL queries.

It is a fundamental shift, and there is often a real fear about the potential loss of control. Doc Searls, co-author of The Cluetrain Manifesto and author of Intention Economy, likens it to the 1980s when mainframe-centric IT departments fought against PCs being introduced and the 1990s when HR departments opposed employees gaining Internet access.

Historically, retail managers’ most significant business decisions were capital intensive with long cycle times – enter a new geography or market; build a new distribution centre; or open 20 new stores. The final decision was based on careful research, usually by some expensive analysts. Today, two things have changed in the decision making process:

A) Shorter cycle times – We simply add capacity in the cloud or launch via an online marketplace. Today’s business is driven by many smaller and specific steps, each of which is measurable.

B) Cheaper cost of analysis – There are more data, more tools and more skills available to carry out analysis. Entering a new market no longer requires a market segmentation by an analyst firm and locally based advertising; today Facebook Graph advertising does it for free, in hours rather than weeks.

These same shifts in data use can be seen in Formula 1. Telematics now send back data as the car is driving, not after the race, which allows the engine to be adjusted continually throughout the race. Retail is rapidly transforming its pace of decision making in the same way.

The data champion

How data is thought about, gathered and used is a strategic decision for every organisation, and should be driven by a data champion from the top – but where at the top? The CIO, as the data protector, works to keep people away from the data. The CTO, responsible for the integrity of systems restricts system access. While the CFO is concerned with reporting using as little information as possible!

Many retail organisations, perhaps inspired by Amazon, have created a Chief Scientist role. This role reverses the scientific method by focusing on asking questions rather than finding answers. Answers, like data, are commodities. Being able to ask the right question is the creative element that will allow you to set your business apart. While this role is a major step forward in developing a data mindset, the Chief Scientist cannot be the data champion. The scale of cultural change requisite to become a truly data-focused organisation must come from the CEO. It’s a massive shift to make every employee customer-centric, and encourage them all to actively gather and use data to drive the business.

Get more data

Many retailers are overwhelmed by the amount of data they have today; we argue that it’s not enough! Having a data mindset demands the continuous search for more data and more ways of using that data.

How can you get more data?

• Start with your customers. Make it easy for them to tell you more. At Amazon Bezos believed in removing all barriers to contribution and so we allowed customers to write reviews without a sign-in.

Decision Intelligence. The Amazon way: A blueprint for success

• Use keywords and phrases from site searches – they can help stock control and product indexing, and over time help to decide what new products to add or where money can be made running PPC advertising for other retailers.

• Review internally what technology is needed to help every part of the business contribute to the data pool. Can you install in-store cameras to examine queuing and checkout and re-deploy resources real time to minimise customer wait time? How can shrinkage be measured in the supply chain or store? Can we use predictive analytics to determine where theft is likely to occur next?

• Identify external sources of data that will provide new competitive insights. The Social Graph is a great source of data about customers and their social networks and the online advertising game is now allowing retailers to target ‘look-alike’ audiences. What would happen if adjacent retailers were able to share information in a co-optition model?

Looking outside retail, there are also plenty of businesses using data exhaust (the data produced as a by-product of another activity) to great effect. Google indexes the web and allows people to search it for free. This data exhaust is an aggregation of search words which are then used in an Adwords auction search term to advertisers. LinkedIn allows people to upload, store, update and share their CVs. This data exhaust is an aggregation of the movement of people between companies, which recruiters pay to advertise and find potential candidates.

Give more

It may feel counter-intuitive, but you should share your data with your suppliers and partners, as well as your customers. It will empower decisionmaking all along the chain.

• To suppliers: Sharing data with your supplier network will help them action improvements and optimise processes to provide a better service. For example, Walmart shares its sales data with its suppliers to help them better predict demand and be proactive in ensuring availability.

• To customers: Guide your customers buying decisions. Sears Holdings has a large base of customer data that they offer to other retailers implicitly via ShopYourWay and explicitly via Metascale. Rather than intrusive push campaigns, customers are presented with products that are relevant and perhaps outside the Sears’ assortment while they are browsing. Sears also benefits from getting feedback into their online marketplace on which new products to offer. Use your data to improve your processes

Design your processes to capture more data so that you can further improve your processes. Amazon actively harvests consumer intelligence. For example they regularly examine on-site search terms as part of the process to improve product descriptions.

If you put the right system in place, like the Social Data Intelligence Test, your products can improve directly from customer data. Customer service should be a profit centre, not a cost centre. If customer feedback data is provided quickly and easily to buyers, suppliers and designers they can respond rapidly.

Online retailers use natural feedback loops such as customer reviews and crowd-sourced support forums that allow customers to engage with them and simultaneously improve the product or experience. For instance, Sony Entertainment uses the gaming feedback boards (e.g. IGN) to determine what features customers love and hate and to work out the optimal time to launch. Google maps experienced a problem with users hacking into their system and turned this into an opportunity by opening up the system, allowing people to contribute – which has allowed for a better product.

Introducing a data mind set is a cultural shift for many retail businesses. The CEO has to introduce a programme of behavioural change where every decision and every meeting is led by data. It should be expected and indeed demanded. Early activities to get you going may include: openly acknowledging data-led successes – where has money been made or saved?; cataloguing initiatives which are explicitly data-led (either new analysis of existing data or collecting new data); or explicitly gathering and sharing widely the data generated from every new product or service launch.

This article by Andreas Weigend (Director Social Data Lab) and Gam Dias (First Retail) originally appeared in Decision Intelligence Issue 8 from Ecommera.

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