Data Analytics: Data In, Decision Out

Imagine you’re strolling around in a mall and suddenly a message pops up on your mobile offering you the latest discount offer at your favourite apparel store. If you love movies then you can describe it as “Serendipity -2” or “Luck by chance -2” (in the desi style).

 

All this is not a coincidence but a well thought out strategy which many retailers are employing to woo their customers. This concept is called “location based marketing” which is being made possible through the “application of data analytics” on huge chunk of data. This data hasn’t been mined out by doing anything extraordinary but it is the compilation of the day to day transactions which we have with the retailers.

Let’s begin with an analogy between the popular “CID teleseries” and the topic of our discussion i.e. data analytics wherein the former had a 100% accuracy rate in solving crime mysteries while the latter reaches to conclusions by examining data in a structured way. Both seem to be having a never-ending affair with a lot of scope for analysis.

image:freedigitalphotos.net

As per definition, the term “Data Analytics” refers to the unlocking of vast amount of data that is generated every day to uncover hidden patterns and preferences of consumers and other useful information for the companies to derive a new meaning for their business. The impact of its applications could result in higher operating margins, productivity and overall profitability, even in those sectors where margins are notoriously tight.

Let’s dig deep into the practices involved by looking at the retail sector (for starters) and then move along to the various other avenues where data analytics is being used and the impact it is creating. The topmost retailers across the world (Walmart, Tesco etc) have become experts in slicing and dicing the data collected through the interaction with their customers. The resultant data levers fall in five main categories namely marketing, merchandising, operations, supply chain and the new business models.

Marketing:

• Cross-selling: This technique uses the customers’ data (demographics, purchase history, preferences etc.) and prompts “you might also want this” in order to increase the average purchase size. E.g. A shopping website recommending products that are related to our purchases.
• Location-based marketing: As explained earlier, it relies on increasing use of smart phones by targeting customers who are either close to the store or already inside them through promotional schemes.
• In-store behavior analysis: By tracking the customers’ shopping patterns and drawing their real time location data, shopkeepers/retailers are improving their store layout, product mix and shelf positioning.
• Sentiment Analysis: It leverages various forms of social media and takes informed business decisions accordingly. E.g. based on the results, proper campaigns can be launched to understand real-time responses.
• Enhancing the multichannel consumer experience: This tool can be used to enhance customer loyalty by targeting the right customers for their promotion based on their preferences and behavior. E.g. sending newsletters based on the customer engagement exercises to drive the future demand.

Merchandising:

• Assortment Optimization: It helps in deciding which products to carry based on buyers’ perception, local demographics and other data thereby increasing retailers’ profit margins. This can also result in rationalizing the SKU count and having the right product mix.
• Price Optimization: Based on the price elasticity of various customers, a variety of data sources can give information about the pricing decisions of a retailer. This system can also be used to evaluate complex relationships for various SKU’s to figure out minute details at that level. E.g. rural consumers are more sensitive to wheat and give them high buying priority than their urban counterparts.

Operations:

• Performance transparency: The real time granular reporting allows retailers to make timely and concrete adjustments in their operations thereby increasing the overall efficiency in their functioning.
• Labor inputs optimization: To have accurate predictions about the staffing needs and better labor scheduling, this lever optimizes labor inputs through automated time and attendance tracking.

Supply Chain:

• Inventory Management: The advanced analytics datasets improve stock forecasting by combining data like stock history, weather predictions and seasonal sales cycles and thus results in best-in-class inventory management for the retailer.
• Distribution and logistics optimization: Transport analytics help in optimizing fuel efficiency, vehicle routing and driver behavior. E.g. using GPS enabled systems.

New Business models:

• Price Comparison services: We can achieve price transparency by offering near-real-time pricing comparison between multiple retail outlets and thereby gaining customers’ loyalty. E.g. by scanning the bar codes we could compare the prices at various outlets.
• Web based markets: Through searchable product listings and price transparency, we can access a much larger customer base. Additional features like the customer generated reviews further add value to the customer by increasing transparency in our dealings. E.g. services provided by Amazon and eBay.

And if we thought that data analytics usage was limited to the retail space only we couldn’t be more wrong. Data analytics has found its footing in arenas as diverse as politics and sports, where it is aiding strategy formulation in a big way. It has the potential to transform businesses. In the case of PayPal, analytics helped discover opportunities which couldn’t have been recognized as such. It helped diagnose revenue leakages in their existing system and also helped analyze the effect of website redesign on customer acquisition.

Analytics is being used in a big way at Wimbledon. Every serve, smash is being monitored and their analysis in conjunction with historical data is helping players stay one step ahead of the competition. Data analytics is revolutionizing the way the US presidential campaign is going ahead. Businesses truly have many things to learn from the innovative applications of big data in the political realm. New data analytics tools are helping gain insights from mails and tweets. Data analytics was successfully used by the Transport for London (TfL) to manage the transportation system during the recently concluded London Olympics.

There was a time when successful businesses were run on experience and instinct. But, a recent study by IBM Global Business Services suggests that successful companies are twice as more likely to be using analytics to aid their businesses than the not so successful ones.

But, it is important to remember the fact that analytics alone is not a wonder pill. Only when it works in close conjunction with market research, strategy, finance and other functions that the optimum benefits can be reaped. More and more companies are putting together a separate analytics team which emphasizes the increasing importance of this function in the overall scheme of their businesses.

Also, like every new tool on the block, there exists a flip side to this. The notions of privacy are being altered and that too in a big way. It remains to be seen as to how the customer reacts to the use of personal information, like personal location data and internet surfing details etc. People remain uninformed about how this data is being used for targeted advertising and other marketing activities. Marketing executives have to keep in mind the legal and the privacy aspects in mind before formulating strategies.

Cloud computing and data analytics are the two buzz words in the industry today. Combine these two and we can alter the way companies do business. This amalgamation is likely to happen in the near future but the companies who are quick in doing this stand to gain the most.

The world used more than 7 Exabyte of disk storage capacity in 2010 (Source: Avendus Capital report).There is an enormous amount of data lying out there, more than enough to confuse the biggest and the best analytic tools and experts. The fact is that we have got all the data, the skill lies in making the right use of it, period.

Not surprisingly, power of data analytics brings in more revenue for a business. A research project by Texas University shows that when the usability of data in a Fortune 1000 company is increased by 10%, it leads to an increase of more than $2 billion in revenues every year. And, this is too big a figure to ignore. In conclusion, organizations that enhance and make use of their big data muscles will improve their chances of emerging victorious at the expense of those who do not grasp this opportunity in the years ahead.

So, next time when you receive a message on your cell phone informing you about the best deal in the shop next to which you are standing, don’t get surprised. It is data analytics which is at play having the power to transform the way business is done.

This article has been authored by Ashish Gauba & Animesh from MDI Gurgaon