How to Harness the Power Of Big Data and Increase Sales?


Big data is a topic that is currently dominating IT discourse. It refers to the theory and practice of extracting, analyzing, and utilizing information from data sets that are too big to handle by conventional data-processing methods.

Big data has changed the way organizations view information as such. Instead of mining limited data for as much information as possible, big data is focused on identifying large-scale trends. It wouldn’t be an understatement to say that big data is revolutionizing business as we know it.

Big data is changing the way prices are determined, handled, and disseminated through sales networks in order to generate the most gains. By using big data algorithms and machine learning techniques, businesses have gained the ability to set the optimal market price for their products and services. Big data has also helped with streamlining seasonal pricing decisions in goods-driven industries where products are less elastic.

Sales is one of the areas where big data is causing the biggest shakeup. Big data is now being used at practically all levels of the sales process, from enhancing the accuracy of prospecting lists to improving sales lead data, increasing sales lead quality, streamlining sales territory planning, discovering sales lead engagement and others.

Big data has had a major impact on digital marketing as well. Marketing agencies, departments, and individuals are harnessing big data to improve the effectiveness of content at each stage of the conversion funnel, increase conversion rates, improve marketing ROI, and customer lifetime value.

In summary, big data has the power to predict consumer behavior by finding patterns scattered across large volumes of data. Naturally, this makes big data especially effective at improving sales. There are many ways in which businesses can adjust their sales workflow through insights gained from big data. And in the rest of this post, we will explore the 10 most prominent ones as of the time of writing.

1. Customer Analysis Techniques

According to research conducted by DataMeer, the most common use case for big data among businesses is customer analysis. They found that:

  • 48% of the sample companies use big data for customer analytics
  • 21% use it to reduce operational risks
  • 12% use it for fraud detection
  • 10% use it for product innovation
  • 10% use it for inventory optimization

These usage scenarios are embedded within broader strategies aimed at improving customer acquisition rates, lowering churn rates, increasing recurrent customer spending, and improving customer-product fit. The wide-scale adoption of big data for these purposes speaks volumes about its effectiveness for achieving business goals.

2. Pricing Strategy Optimization

According to research conducted by the McKinsey & Company, companies are increasingly using big data to implement flexible pricing strategies at the level of individual customers. They found that almost 75% of revenue generated by companies can be attributed to their standard repertoire of products, but that only about 30% of these products are being sold at the optimum price point. To solve this issue, companies began using big data analytics to find the price that would generate the most revenue. With an adjustment of as little as 1% to the price of an item, companies stand to gain as much as 8.7% increase in profit, without making any changes to their sales pipeline.

3. Improved Customer Responsiveness

Forrester published a study on marketing performance as it relates to customer experience. They found that big data represents a way for companies to reach their marketing goals through greater customer insight. According to the study, 44% of the interviewed B2C companies are using big data analytics to increase customer responsiveness, while 36% have stated that big data was essential for developing effective relationship-driven customer strategies. The logic behind this is simple at its core. The more data you have on customers, the more you know about their needs, wants, and desires. This, in turn, enables you to approach customers where they want, when they want, with a message they’ll be interested in hearing.

4. Enabling Contextual Marketing

Besides enhancing existing sales and marketing strategies, big data has demonstrated the capacity for enabling the implementation of new ones as well. Big data is the cornerstone of contextual marketing, a marketing approach whereby consumers are served with targeted advertising based on their search engine history and browsing behavior. By analyzing large volumes of online behavioral data, companies have gained the ability to aim for marketing messages with laser precision. In effect, big data is blurring the lines between marketing and sales – a carefully crafted ad served at an opportune moment, itself derived from big data analysis, is all it takes to convert a lead into a customer.

5. Sales-focused Company Restructuring

According to a survey conducted by Forbes, companies are experiencing greater customer engagement and loyalty thanks to the introduction of big data analytics into their workflow. The survey showcases that businesses from different industries are using department-focused big data analytics to enact novel sales strategies. This approach is pushing companies to modify their internal structure in order to accommodate said strategies. Thanks to the success of such projects, company culture as a whole is experiencing a change. In order to meet sales goals defined by big data, businesses are increasingly shifting to a data-first approach in their everyday operations.

6. More Successful Customer Relationships

Big data is proving potent at providing customer service reps with insights needed to nurture long-term customer relationships. In the past, customer service was a kind of troubleshooting activity – its goal was to address customer questions and complaints if they happened to arise. With big data, this reactive approach was substituted with a proactive one. Data-driven customer service relies on continuous B2C interaction. Customer service representatives now stay in touch with customers for the entire duration of their engagement with a business. By using customer data to supervise customer development, customer service reps have the potential to drastically improve customer loyalty and customer lifetime value.

7. Streamlining the SEO-Sales Pipeline

Companies rely on search engine optimization to generate a steady stream of leads from organic website traffic. And one of the essential elements of SEO is finding keywords that express buying intent from customers. Keyword prospecting and analysis are tasks whose success is directly tied to the amount of data at a company’s disposal. With big data, it is possible to find and keep track of the most relevant keywords at any given moment. Companies can then use this information to dynamically generate effective sales-focused content. Big data is therefore crucial for dynamic, long-term, SEO-sales alignment.

8. Augmenting Geo-targeted Sales

Companies that rely on regional pricing and sales in their business model are leveraging big data to optimize their selling strategy through geo-analytics. Region-based selling used to require a lot of guesswork on the part of companies, even in situations where ample demographic, cultural, and geographical data is available. Geo-analytics derived from big data bypass this issue by tracking the location-based activity on a massive scale. This helps companies acquire a clear picture of how customers in particular regions are behaving in practice, so they can adjust their sales models accordingly. Adopting region-specific sales strategies is becoming increasingly important with the rise of consumer markets in China, India, and many developing nations.

9. Customer Value Analytics

Customer Value Analytics is a new marketing discipline based on big data. It enables marketers to deliver synchronized customer experiences across multiple channels, including email, website, and social. This multi-pronged approach to marketing is already proving effective at shortening sales cycles while harnessing the benefits of a personalized approach to customer relationships. Customer Value Analytics works by segmenting customers into different value tiers by on a set of attributes or characteristics. The goal is to create a set of characteristics that define what is a high-value customer and then segment the existing customer population from in order from least to most valuable. These high-value targets are then systematically targeted across different channels. This kind of coordinated strategy is only possible thanks to big data analytics.

10. Greater Upselling Opportunities

The most straightforward way to boost sales is to pursue upselling opportunities whenever they are available. Once you have access to a customer’s personal information and purchase history, it is an easy matter to find additional products and services they might find interesting. Big data is giving companies the opportunity to streamline this process by predicting the most likely upselling opportunities and scenarios. By having access to precise upselling suggestions, companies can capitalize on upselling opportunities they would have otherwise missed. The same principle applies to cross-selling – with big data, it is equally easy to find products and services related to the initial purchase.

Growing Sales with Big Data

Thanks to modern data mining techniques, it is possible to amass tons of customer data, but utilizing it in a meaningful way is another matter entirely. Companies are still exploring the venues by which big data can be used to increase sales, and their research is slowly bearing fruit. The techniques and strategies we have outlined above are the products of this research, and they are quickly becoming the new industry standard when it comes to data-driven selling. Study them, and consider trying out the ones that are suitable for your organization. Big data is the future, and the sooner you adapt to the new paradigm, the better position you will be once big data becomes the new norm.

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