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Predictive Analytics and Big Data - The Future of B2B Sales

Today, the world belongs to big data. For business corporations and multinational companies, big data is the true-blue superpower that bestows them with the gift of looking into the future. In the past few years, big data has not only got bigger but also a lot smarter - powering predictive analytics to the top of the most wanted list for businesses worldwide. Predictive analytics is an algorithm-based discipline which inundates businesses with a steady stream of actionable data insights, acting as the crystal ball which delivers future insights but with the addition of allowing you to act on them and turn negative insights into positive outcomes.

Today, predictive analytics is everywhere - from targeted advertisements to consumer recommendation engines, delivering significant improvements to an organizations' growth, efficiency, and overall effectiveness.

Why Does Predictive Analytics Matter for B2B Sales?

Predictive analytics leverages big data, machine learning techniques, and statistical algorithms to identify the future outcomes and their likelihood by taking historical data into account. With its help, businesses can truly understand which important decisions will strategically affect them in the coming years. Historically speaking, there has always been less buzz associated with predictive analytics in the business-to-business sphere. But this thinking is changing for the better as even old-school B2B firms dealing in commodity products start realizing its many benefits.

In a recent survey conducted among 1000 B2B sales organizations, more than 53% rated themselves as being expert users of big data-based predictive analytics. Most of these businesses feel that the complicated nature of B2B sales and predictive analytics is a match made in heaven. Today, a growing number of B2B companies are adding new services and providing better value to their customers by using data and analytics for better service delivery. By delivering better product quality and flexibility, such companies are able to lift the value proposition that they bring to the table and offer customers more than functional and economic elements of value.

How Advanced Analytics is Changing B2B Selling

The role of predictive analytics in B2B sales operation is extremely important and can deliver massive amounts of value when implemented properly. With the amount of data available at hand, predictive analytics is transforming sales forecasts and relegating manual guesswork to the bin, thereby increasing reliability and accuracy across the board. As a business owner, every day you stand to learn something, improving your sales funnel and existing business processes for maximum efficiency.

Let us take a look at how predictive analytics and big data are changing the future of B2B sales -

01. Improved Lead Generation and Lead Scoring

Over the years, analytics has shown that it can improve the overall accuracy of many business processes, and lead generation is no different. B2B companies are leveraging rich data sets to identify where their customers are, who they are, and how they should be approached

Predictive analytics is helping businesses to leverage historical sales information to develop a map of sales prospects around you, or over the whole country.
Lead-scoring algorithms are being introduced based on granular data sets for better prospects. These algorithms combine a customer's history with external data to generate a complex customer persona.
Companies can predict which leads are most likely to close and find which kind of businesses they can service and close better compared to other ones. Focusing on such tactics have resulted in a 30% increase in conversion rate for many companies
Many companies are also trialing with AI-enabled agents that leverage natural language processing and predictive analytics to automate lead generation techniques especially during the 1st phase

02. Better Sales Forecasting

Sales forecasting is probably the most important procedure for most B2B companies as far as sales are concerned. Proper forecasting models can keep things moving smoothly through the year, and any discrepancy in prediction can lead to poor resource allocation as well as overall process efficiency.

With the correct predictive models, B2B organizations the world over can now make the correct decisions when it comes to allocation of the sales resources. When combined with customer behavior models, seasonal demand models, and other algorithms, sales forecasting becomes a powerful tool for business growth. These improvements can also directly impact stock replenishment and lead to lower customer attrition.

03. Better Matching of People to Deals

Traditionally, B2B salesmen have always performed sales planning by relying on to account segmentation. This further depends heavily on historical local knowledge, as well as up-to-date facts. As a result, over time, sales models tend to become ineffective and provide inconsistent results, leading to poor resource allocation and multiple different sales strategies without any common point.

Therefore, when predictive analytics is introduced into sales planning, resources can be effectively allocated to the right projects. At the same time, predictive analytics for B2B sales can do much more, including changing the way businesses look at sales talent and field expertise. Today, organizations are not only searching for high-performing salespeople, but also combining customer, sales, and HR data to hire and retain salespeople and allocate them to accounts for which they hold the expertise. Data analytics can also reveal hidden traits in the high-performers, allowing HR to hire people with better skills relevant to what the company needs. This process also termed as predictive pipeline management can reduce the cost associated with sales by 6-10% while boosting revenue across the board.

04. Better Customer Lifetime Value

Many B2B companies do not have a traditional portfolio, and therefore, find it tough to find perfect solutions to customer needs. Salespeople often have to undergo time-consuming interactions that can lead to missed opportunities when trying to sell their products. As a result, many businesses are incorporating algorithms which suggest the salespeople about what similar customers have bought in the past. This is extremely useful as it can also be used to identify cross-sell opportunities and under-served customers.

This approach also can help retain customers by recognizing signs of customer discontent and ensuring necessary action is taken much before they take their business elsewhere. ML-based algorithms have expert pattern-recognition skills and can easily identify when cross-selling can actually help customers stay on board for a longer time. Armed with such insights, you can reduce churn and help your sales department succeed.

05. Improved Pricing Strategy

Any salesperson would admit to the fact that the price negotiation in the B2B world is an extremely time-consuming process. With the help of big data-powered analytics, this stands to change though. Today, special algorithms can provide enhanced price transparency and allow sellers to make complex deals while negotiating. While experience plays a big deal on the negotiation field, deal analytics can help the team deploy sophisticated pricing tools by bringing in dynamic deal scoring techniques which place relevant information in front of the sales rep during negotiation. They also receive immediate data about similar purchases made historically, and the deciding factors which helped level the playing field.

Why Should My Business Use Predictive Analytics?

Simply put, predictive analytics just works. It can transform sales forecasts by removing the guesswork and using actual science to do the heavy lifting. The insights gained from such analytics can help your business learn new techniques and improve your sales process efficiency. And since all this takes place in the background, you never have to spend too much time looking at the dashboard or tweaking things to suit your requirement. With the help of predictive analysis your business can benefit from the following -

Enhanced Productivity - You get access to data that can be readily used for analysis, thereby making your sales team more proactive. Since the predictions are based on past data, you can leave out assumptions when dealing with clients
Reduced Costs - Since your processes always stay up-to-date with what your customers require, you can save money by not having delayed processes and long-standing inventory problems
Fewer Resources - Predictive analytics can always lead you towards your desired outcome, thereby helping you save on any misallocated resources and saving on cost and time.
Immediate Results - Predictive analytics can help you capitalize quickly on future trends by emphasizing new developments and varied customer acquisition models based on your current performance.
Better Quality and Functionality - Industries such as the business hospitality industry leverage predictive analytics to enhance their decision-making process, give better prices to recurring users, and keep them happy with frequent perks
Enhanced Risk Management - Predictive analytics always help you stay prepared, allowing you to respond to challenges before they become threatening while allowing you to nullify them in real time
Optimized Marketing - With the help of predictive analytics, you can quickly grasp your customer's behavior, and optimize your campaigns to suit old, new, and prospective customers alike. This also ensures you always retain your most profitable customers.

Partner with Outsource2india for Predictive Data Analysis Services

At O2I, we believe predictive analysis is your answer to the rising challenges of the B2B world. We have worked hard to widen our data science offerings while keeping our client's best interests in mind. With our significantly deep and broad experience in providing a variety of different offerings which leverage big data models and predictive analysis, we can help integrate data from your CRM and leverage it to build models which help you achieve your desired result.