Donal Dunne

Donal Dunne

Can Big Data Live Up To Its Promise For Retailers?

Can Big Data Live Up To Its Promise For Retailers?

Can Big Data Live Up To Its Promise For Retailers?

Can Big Data Live Up To Its Promise For Retailers? [/caption]Was Sam Walton, founder of Walmart talking about Big Data and Analytic in the 90’s when he said, “People think we got big by putting big stores in small towns. Really, we got big by replacing inventory with information.”  Walmart clearly understood the value of the large volumes of data they had access to and turned it into competitive advantage.

As retailers move from looking in the rear view mirror (what happened) to the road ahead (what will happen) they have turned to Big Data and Analytics for answers. While, Big Data holds great promise for retailers, many are skeptical. Retailers are already drinking from the data fire hose, whether its transaction data, recording every product sold to every customer across all channels or research data, covering detailed consumer profiles or web log and social data. The questions retailers are asking; will the investment drive more revenues, increase customer loyalty and create a more rewarding customer experience? Will I gain a deeper insight into customer transactions and interactions across the organization? Can we use existing resources and infrastructure?

The answer is Yes, Big Data presents the opportunity to better analyse everything from customer shopping behaviors at each stage of purchase journey, to inventory planning to delivering relevant and personalized offers. By analyzing how shoppers found your products, how long they spend browsing product pages and which products they added to their basket provides greater insight into what decision process they went through before purchase and helps retailers quickly identify cross sell and up-sell opportunities in real-time. In addition, combining transaction data and what your customers are saying on social channels (ratings, likes, dislikes, what’s trending etc.) can feed into the decisions you make on placing the right product, in the right store at the right price and ultimately deliver very personalize and contextual offers to the customers.

Data Driven Decisions Getting value from Big Data

Turning Big Data into actionable insight is not just about dumping data in to a “Data Lake” and pointing an analytics tool at it and saying job done!  Retailers need to take a number of steps to profit from Big Data and Analytics.

  • Firstly, you need to gather data from all available sources in batch or real-time, from internal and external, and from an ever increasing number of devices (beacons, mobile devices). Once you have gathered the data, it needs to be connected, validated, cleansed and a governance process put in place before integrating with analytic tools and systems.
  • Secondly, put clean and trusted data in the hands of data scientists who can distill the relevant from irrelevant and formulate commercial insights that the business can action and profit from it.
  • Lastly, plan and organize for success. IT and business need to align behind the same agenda, regularly reviewing business priorities and adjusting as needed. Maximize existing scare IT resources by leveraging existing technologies, Cloud platforms and forming alliances with 3rd party vendors to fill skills gap. Secure quick wins for your Big Data initiatives; maybe start with integrating historical transaction data with real-time purchase data to make personalized offers at point of sale. Look outside your organization and to other industries like retail banking or telecommunications and learn from their successes and failures.

With the right approach, Big Data will deliver the return on investment for retailers.

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Bad Data Is Criminal

Reading a recent central statistical office report showing that burglaries, theft and fraud have increased in the last quarter reminded me of a how Humberside police use data and the information they extract from it to help fight crime.

It is easy to underestimate how tenacious the modern criminal can be in avoiding attention and capture by authorities, they are not motivated to provide accurate information when questioned! Moreover, any inconsistency amongst police departments or personnel in the way in which the data was entered could add to data quality problems, inadvertently aiding the criminal. (more…)

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Data Quality Helps Find Missing Revenue

The establishment and maintenance of accurate customer data is the key to all revenue-generating events that a company has. A single key question is at the heart of this: Do you understand your customers? And good quality data is at the heart of the answer to the question.

As an extension every organization must know who its customers are, what do they want? What did they buy?  This question appears straightforward, but it’s not uncommon for every department within a company – finance, sales, marketing, or customer service – to have a different answer because each has their own version of the customer data. (more…)

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Posted in Customer Acquisition & Retention, Customers, Data Governance, Data Quality, Telecommunications | Tagged , , , , | Leave a comment

Beauty Is Only Skin Deep

“Beauty is only skin deep”. This old saying came to mind recently when speaking with a friend. Jane was relaying to me the amount of time she spends correcting data for management reports every month. Answering simple questions like “how many new customers did we add?”, “how many customers placed repeat orders?” or “what was the top selling product?” Without the correct answers, management ran the risk of making poor decisions on future investments in marketing campaign, capacity planning and sales and support resources.

On average Jane spent two days a month checking for duplicate customer names and standardising product codes and descriptions, just so the reports would give an accurate reflection of sales. All this was managed in multiple Excel worksheets. The reporting tool the company had invested in was still being supplemented with manual worksheets, as management did not trust the information from the tool of choice. As the months and quarters went by Jane spent more and more time managing the worksheets. (more…)

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Posted in Data Integration, Data Quality, Profiling | Tagged , , , , , | 2 Comments