Awful puns aside, Big Data is a truly revolutionary opportunity for the enterprise salesforce. Till recently, big data was described as an opportunity–an avenue, a skill set that enterprises could leverage to increase revenue. However, the perception of big data has gradually changed. It is now not seen as just an opportunity, but a necessity. Enterprises now increasingly realize that leveraging big data is essential not just as a competitive advantage, but as a key survival skill. While big data is impacting businesses at all levels, particularly interesting is the way big data can impact sales, and the ways in which enterprises can leverage big data to empower the sales force and achieve an increase in sales.
As a result, there is certainly a big gap between market leaders with big data and companies that are still relying only on traditional methods to drive their sales efforts. Considering the value it offers, it is not surprising that the use of Big Data for additional customer wins is becoming more and more mainstream.
Moving beyond the buzzwords, we certainly identify very specific ways in which big data helps a sales force achieve greater efficiency. One well-known avenue, of course, is using the vast amounts of data from social networks. Combined with other data sources, this can become a powerful tool.
Three key sources are customer data, mobile data, and social data. Together, this data creates a dynamic profile of customers. And, in turn, sales teams can leverage the intelligence offered by this data to achieve an increase in sales. The starting point, of course, is customer data: the data that customers have themselves explicitly handed over: purchase behavior and preferences, name and age, and the like. When this data is combined with other information made available from customers’ mobile devices, the data becomes even more specific, context and location sensitive, and far more powerful and actionable than plain customer data. A key strategy that enterprises deploy is to further add the data from social networks into the mix, thereby ending up with a powerful information base that helps enterprises identify specific revenue growth paths, problem areas (current as well as forthcoming) with their product lines, and more.
This almost sounds like the process of triangulation, and offers results that are equally precise and focused.
Big data Analytics for Retailers
According to a report by McKinsey, retailers can achieve an increase of as much as 60 percent in their operating margins by accessing information of inventory, sourcing, social connect with consumers, and other data to form the digital relationship with consumers. The source of this big data is through point-of-sale (POS), where retailers have access to all the information about customers which can improve the bottom-line performance. This analytics data provides valuable insights, particularly with regards to purchasing behavior: What do people buy, when, how often, at what time, and more. How far do discounts and offers affect purchasing behavior? How do factors such as customer location, product placement, membership and discounts, impact a customer’s buying decision? This intelligence is so powerful that it helps drive promotions directly to valuable customers.
Today GPS location awareness offered by smartphone technologies offer opportunities for retailers to engage customers through promotional messages that are highly important to a specific time and location. Imagine, for instance, a shopper standing in front of an electronic store receiving a text offering a discount on a certain brand.
Big Brand Using Big Data
General Motors (GM) is today combining big data analytics and geographic information systems (GIS) to model dealership performance. This enables dealers to view local demographics, location characteristics, regional differences and the competitive environment. This helps GM to make intelligent predictions regarding how a dealership should be performing compared to the actual results. The GIS system has also allowed GM to add locations to analyze the equation, so that they can now map everything effectively.. GM now knows who and where the buyers are that buy luxury brands, and who prefer midsized. Because of this detailed demographic data to marketing, the automaker can direct its ad spend to the right areas with fine precision.
Another excellent example of using big data effectively has been set by Walmart, the American retail giant. Walmart collects 2.5 petabytes of data from more than 1 million customers every hour which is equal to 167 times the books in America’s Library of Congress. The analysis covers millions of products and hundreds of millions customers from different sources. Walmart observes what customers buy in-store and online, what are the trends on social media, how weather conditions can affect the buying patterns, and more. The analytics systems at Walmart analyse close to 100 million keywords on a daily basis to improve the bidding of each keyword and this has resulted a significant 10% to 15% increase in online sales for $1 billion in incremental revenue in 2015.
IKEA, the world’s leading furniture retailer, prints more than 200 million catalogues in 65+ languages each year, which is driving millions of people to their websites and offline stores. IKEA is offering personalized digital content in its catalog for image recognition and Augmented Reality for product views to customers, and have combined the benefits of in-store and online shopping. With IKEA’s image-recognition technology, readers have to scan the catalog from their mobile devices to see the furniture displays and customers can also “virtually” place the IKEA furniture pieces in their own homes to choose the colors, products, and sizes that work, without going to the store.
Using it in a right way
- Personalize the Customer touch
Every interaction with customer is vital, because any negative experience can lead them to other brands. The strategy should be customer-centric, collecting the data relentlessly can hurt customer trust and impact ROI negatively.
- Holistic journey for Buyers
Big Data has a role to play to in helping enterprises understand exactly what the customers want, identify the potential customers, but also to craft meaningful experiences that bring people back to the content and into the sales funnel.
- Accurate Forecasts
Real-time and predictive analysis can boost the successful sales. Once data is collected and implemented, businesses can forecast customer behaviors effectively
Leveraging big data to increase sales, particularly in the retail sector, is all about understanding the correlation between sales and factors such as the weather, culture, social media trends, competitors and consumer sentiments that empowers improvement in the financial performance of the company. Companies that leverage big data, respond to market shifts, engage consumers, and design products that consumers will embrace, predictably tend to fare well.
Beyond retail, organizations in many industry sectors such as Healthcare, Public Sector administration, Manufacturing etc. are leveraging big data to improve their portion and coordination of physical and human resources, cut waste, and increase transparency, accountability, and ease the discovery of new ideas and insights.