Sales – the exchange of goods and services for money or other consideration, is the basis of any business. How do businesses sell products? There are multiple methods (distinguished by sales channel, consumer type and other factors), which include direct sales, agency-based sales, door-to-door sales, electronic sales, B2B sales, B2C sales, and more. Underlying all of these methods is the necessity of knowing who to sell to, what to sell to them, when to sell to them, and where to sell to them. Why a buyer decides to make a purchase is critical to answering these questions that aid in the process of making sale. In sum, the more information a salesperson and/or firm have about a customer or a customer segment, the greater the ability they will have to tailor a sales strategy that impels a consumer to buy the good/service in question.

Big Data, which has evolved from business trending topic, to a widely acknowledged (though inconsistently adopted) strategic orientation across industries, is all about providing the right kind of information to firms, departments and professionals. As such, Big Data holds tremendous potential for augmenting sales processes and procedures, and ultimately, increasing a firm’s bottom line. Sales departments across the country have long realized the importance of data-driven approaches to sales – many firms use customer relationship management systems (CRMs) to aggregate, filter and analyze its interactions with leads. However, Big Data analytics takes CRM data and tools to the next level, with tremendous benefits for firms, which adopt and implement them properly.

Initial Guide for Using Big Data in Sales

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In this article, we will cover 1) the benefits of enhancing sales with Big Data’s tools and techniques; 2) sales forecasting and target marketing using predictive analytics; 3) gathering lead intelligence and lead scoring; 4) converting leads to sales through customized sales experiences; 5) increasing lifetime customer value; and 6) better salesforce management. 


Big Data – both, (1) the volume, variety, and variety of digital and digitizable data from internal operations and external sources, and (2) the ever-evolving set of tools, techniques and technologies designed to aggregate, store, filter, and analyze said data, can yield rich insights about all aspects of a business. This includes a firm’s consumer base, product line, and distribution chain, all of which the firm can leverage to imbue its products with enough perceived and actual value to entice customers to buy. The firm can use these insights to manage marketing resources more efficiently, by helping marketing departments identify and target the most profitable potential customer segments, funneling them to the sales force. Further, Big Data-gleaned insights can be used to manage individual and salesforce performance, adjusting approaches where necessary to meet or exceed Key Performance Indicators (KPIs).

To harness Big Data requires investments in hardware/software and personnel beyond the deployment of a CRM. For Big Data includes data from every operational aspect of the business – from accounting to manufacturing to human resources. Big Data also necessitates the hire, insourcing or outsourcing of both IT personnel skilled in the processing and storage of large volumes and varieties of data in real-time and near-real-time; and data scientists trained in both filtering and analyzing the data, and sharing actionable insights with key actors across the firm. Integrating Big Data management systems with legacy CRMs may require additional outside resources. However, integrating and leveraging Big Data into a sales operation successfully requires vision, buy-in from the top, and, ultimately, firm-wide adoption of Big data management tools and techniques.

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Predictive analytics – a powerful toolset of forecasting techniques, allows firms to determine sales revenue goals, and the probability of achieving them in real-time. By continually measuring performance and adjusting tactics where there are deficits, increases a firm’s probability of achieving and surpassing those goals.

Predictive analytics allows a firm’s data scientists to create mathematical models of consumer behavior based on historical data; such sales data might include prior purchase history, customer acquisition costs, advertising/marketing messages received by the consumer, and other similarly relevant data. These models can then be used to predict, within a particular degree of certainty, the probability of the purchase behavior changing or remaining the same. Depending on the capabilities of the firm’s Big Data architecture and personnel, this can be performed in real-time or near-real-time, allowing sales managers and sales personnel to continuously revise and adjust their selling strategies to achieve sales targets.

Models can also be built around most any other type of sales question, providing sales personnel further insights on approaching clients. These models need not be built with purely internal data. They can be built with external data, or with a combination of both. Building models with external data can be particularly helpful for firms when forecasting the effects of external and/or global events on a firm’s sales. For example, a two hundred year firm with advance notice of a hurricane, may examine how well they have historically performed in the aftermath of a hurricane, as well as how similar firms/competitors have fared. This can assist in accurate projections that benefit not only the firm’s sales personnel, but also stakeholders, such as shareholders, as well.

Big Data management systems are designed to deal with greater volumes of data and greater varieties of data types than traditional CRMs. With a strong data science team, and robust management systems, the increased data can be harnessed for greater consumer analysis.


Big Data management systems are designed to deal with greater volumes of data and greater varieties of data types than traditional CRMs. With a strong data science team, and robust management systems, the increased data can be harnessed for an improved analysis of consumer trends.

Data mining enables firms to develop effective target marketing strategies, especially in B2C sales. By aggregating historical internal consumer data, and external data from a variety of sources, firms can target and qualify consumer leads before the first marketing dollar is spent. Using this data, firms can forecast a customer’s probable purchase behavior with a high degree of accuracy. This allows sales directors to identify and refine key target segments and/or leads, inform the marketing strategies most effective in reaching them, and determine the strategies that will convert them from leads to customers.

Marketers can accomplish these goals through message testing, promotional strategies, and even product mixes. Loyalty programs provide great test beds for marketers, who can glean actionable insights into purchase behavior, purchase behavior, and price sensitivity, among other crucial factors. When a firm’s marketing efforts are optimized, the sales team, ideally, is exerting minimal effort on a large pool of interested customers. They can then spend their time on harder-to-reach, and perhaps more lucrative customers.

In B2B sales, the universe of possible leads is smaller, and the purchases are also larger in absolute dollars. Further, the ultimate decision maker in purchase decision is often unclear initially; and/or, multiple individuals make decisions jointly. Accordingly, customer acquisition costs are often higher; therefore, sales departments must ensure they are not burning through capital on failed conversions. Big Data is a great source of lead intelligence; whereas historically, lead intelligence might have consisted of industry reports, and firm-lead interactions (calls and visits), lead intelligence in the era of Big Data can include everything from social network usage of purchasing directors to their behavior on the firm’s website. Lead scoring is used to rank leads based on their potential value; sales departments can then allot the appropriate amount of time, effort, and marketing/sales dollars towards the most highly ranked leads. 


Big Data not only provides sales personnel with insights about how to approach consumers/leads; when properly managed, it provides them with the capability to provide consumers with personalized sales experiences. CRMs already aggregate data; Big Data management systems allow a greater volume and variety of data to be integrated in real-time, and near-real-time. Using this information allows salespeople to develop a more complete view of each consumer and conduct direct sales in a highly personal manner. It allows digital marketing/sales teams to offer personalized recommendations drawing on the individual’s complete history with the firm (everything from customer service calls to office visits to online community activity), as well as the opportunity to use this information to upsell. Additionally, it gives cold callers a wealth of information with which to start a conversation. Personalization deepens brand engagement. Highly engaged customers are likely to be repeat customers.

Of course, even the largest multinational firms lack the manpower to manually construct personalized sales experiences for every single consumer it touches. Big Data’s technologies include automated personalization systems that funnel digital consumer data into personal electronic communications (including, but not limited to emails, social media messages, and landing pages) as soon as the consumer fulfills certain criteria (such as registering on the firm’s website). Further, these tools allow for consistent messaging across all channels – whether direct mail, text messages, or robo calls. Though it is a mass marketing approach, it creates the appearance of a personal touch, which many consumers find engaging. 


Beyond deeper brand engagement through customized sales experiences Big Data can yield, Big Data can increase lifetime customer value in a variety of ways. It can provide the firm with greater account management capabilities, and allow the firm to design products with greater perceived or actual value, as well as allow it to optimize its online and offline sales channels for maximum conversions.

Big Data allows for greater continuity in account management, which is especially important in direct B2B sales. Prior to the advent of Big Data, a salesman or account manager, having developed client relationships, might have pertinent client information, such as preferences and personal idiosyncrasies, that could make it difficult for other sales personnel to manage that relationship. If that employee were out sick, the client relationship might suffer. If that employee left the firm, clients might walk out the door with him. CRMs allow client information to be shared across the firm, reducing (though not entirely eliminating) organizational dependency on individual sales personnel.

Big Data can also help the firm understand the psychology of its average consumer better, which can yield insights into new products, packaging, promotional opportunities, and more. Some of these insights are received by sales personnel, but for a variety of reasons, may not be shared outside of the sales department. A proper implementation of Big Data management systems (IT and firm-wide processes) ensures that no matter where consumer insights land – whether in accounts payable, customer service, sales, or manufacturing, they are routed to the right organizational entity, to be harnessed.

It is easy to see how Big Data insights might help an online vendor optimize its landing page for maximum conversion. The vendor can test direct marketing copy, landing page graphics, However, it can also help the vendor optimize the off-page (such as online ads) and offline (such as direct mail or event marketing) factors that might drive traffic to the site as well. Moreover, it allows managers and executives to design physical sales locations that will most likely impel the greatest number of consumers to purchase.

Consider the case of Walmart. Walmart took a wealth of consumer data gleaned from every brand touch-point – from point-of-sale transactions to retail sales history, and used this information to inform the design of their physical retail locations to drive sales. Walmart stores a record of every item a customer buys for two years in its data centers, and uses this historical data to fuel predictive distribution and purchasing models to forecast consumer behavior. This information in turn helps fuels its inventory management systems (as do real-time/near-real-time inventory reports), which allow for an optimal volume of goods. The store locations themselves are chosen for their location along an optimal distribution network. They are also designed to maximize customer value during the shopping experience. The Crestview, Florida store, for example, aligned the most frequently visited departments, increased signage, and added a technology testing area, during a 2010 redesign, allowing for a quicker shopping experience. The Washington, DC H Street location, opened in 2013, eschewed the traditional grey-blue building, which is at odds with a thriving urban locale, for a massive brick building with multiple retail tenants and even residential apartments for rent.


Big Data allows firms to better manage their salesforce through people analytics – the application of predictive analytics to human resources. Not every individual is cut out to be a salesman, and people analytics can help an HR department “predict” the most successful candidates for openings, through the development of mathematical models of the traits of existing star performers. Recruitment software can then mine through resume, social media, and background data, as well as firm-produced skills and psychological tests, apply these variables through the models, and provide quantitative assessments of each candidate. External data mining can also help a firm identify (and then recruit) talent, even before they have applied for an opening. Using these methods can help a firm reduce talent acquisition costs and turnover in the sales department, and acquire revenue-producing personnel.

Big Data’s management tools and technologies can also help managers monitor their salesforces – crucial as sales personnel are often field staff. People analytics can help managers determine the cause of a staff member’s missed numbers, whether they are encountering unforeseen challenges, require additional training, or are slacking off.

Finally, there is often a disconnect between a firm’s field sales staff and marketing department. No matter how sophisticated the method of predicting consumer behavior, the firm may be wrong. It might be a marketing message that did not quite land. Or a product might not satisfy the need which the firm intended. Those who hear it first are a firm’s salesforce; they must also quickly correct a firm’s mistaken assumptions in the mind of the consumer. The pragmatic knowledge of what works and what doesn’t is invaluable to the firm but often goes ignored, to the resentment of sales professionals. These insights can be easily shared firm-wide with Big Data tools, but when firm’s take them seriously and harness them in, say, new product development or advertising, it can have a tremendous morale-boosting effect. Sales personnel, now seeing their insights in action, may feel more connected to the firm and enthusiastic about its brand promise; and the more motivated the salesforce, the more likely it is to achieve its sales goals.

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