In B2b scenarios, most of the time the CIO or CEO of the company makes decisions on technology front. And to name a few peculiar factors which are considered by them before selecting any product or service as a solution are technology adaption capability, cost, duration, CEO/CIO people culture and vendor culture, strong reference etc. So it becomes a prime factor for the sales people to consider as many factors as possible to make a successful deal.

In addition, a few of the many parameters affecting sales in changing technologies are less F2F time, quick decision process, automated marketing intelligence to have edge over others, anticipating change in buying behavior of business, speed of interaction with client, buyers have changed and know much more when they meet sales people and to tell them the process in a simple way.

So the expectation is that a sales person should be a consultant who knows and understands the problem and knows the array of solutions for the exact problem which helps to keep the sales person competitive and at par with the buyer.

Under such situations a dynamic technology really becomes the powerful tool for sales people.

So Big data helps to address most of the above situations in a smarter way. Below are few points which can be handier.

Optimizing Pricing Strategies

Cheese and bread can be thought of as having low price elasticity; but home purchase and jewelry as having high price elasticity. To clarify the concept of elasticity-based pricing analytics, we try to predict the price at which revenues and margins will be maximum or in other words if the price is increased further, then overall profits will decline.

Similarly pricing analytics is trying to define billions of customer data based on commonalities among thousands of variables. For analytics the data to be included are historic quote data, “wins” and “losses”, line item detail, price data, sale cost and discount, product hierarchy, configurations, marginal costs, customer data, sales history, competitive data, competitors by product and by deal etc. Market response modelling(MRM) will include all the above data points as base which will be further refined and optimized using model target price and what if capabilities and finally to provide enhanced model for accurate pricing. 

Then companies need to work closely with their sales reps to explain the reasons for the price recommendations and how the system works so that they can trust the prices enough to sell them to their customers. And to provide a clear set of communications so that a rationale for the prices can be provided in order to highlight the value.

Greater Customer Insight

In B2C the analytics is there for long. But to take similar approach in B2B type of sales we must collect data on what companies buy, how they pay, and how long it takes them to purchase. We also need to use public records and news to get a full picture of prospects’ potential for conversion. And all this is meaningful if it’s done asap. Also we can use lead mapping tactics where it helps sales teams predict lead behavior and better anticipate the tough questions. Tracking digital interactions such as page visits, time spent on site, and downloads etc. also helps to provide requirement of the prospect. Moreover, it is also important to get the details of the buyer’s sphere of influence, a prospective customer must be able to convince the rest of the team that your products or services will help to fill their company’s needs, as well as the purchasing manager, to complete the deal successfully.

A 360-degree customer data is defined as the combination of the following information ● Private customer information which includes email addresses, mobile phone numbers etc.● Public and social profile information which includes job titles, profile photos, bios etc. and ● Contextual customer information such as past customer service interactions, whether someone is a new customer or loyal customer, or how influential the person is in specific social media communities.

Accumulation and analytics of these points makes the sales game much more competitive and successful.

Automated Marketing Intelligence(AMI)

Sales and marketing are making at the top 90% of revolutionized list of sectors across most of the business.

This makes Big Data especially appealing to CEOs and CMOs with spectrum of new flavors of data that can shed powerful new insights into business operations, data oriented speculations, customer engagement, and much more – insights that are meaningless if it takes take months to produce the same.

AMI tolls can use its network for interactions, to uncover opportunities at every stage of the sales and can determines which existing prospects are in market. They can also predict what products prospects will buy, how much they will buy, and when.

Big data also helps to answer the questions like :

–         What is the intelligence telling us?

–         How accurate do we feel that the information is?

–         How current is the information?

–         How can we decipher this information to help us make more intelligent decisions?

Using big data we can check the performance of our digital assets like websites, ads, blogs, videos, social pages etc.

We can also use user behavior based approach for e.g. when are they coming, where are they coming from, what’s driving them to specific web pages, what action is taken once they get there, how long are they staying, when and where are they leaving and why.

Unfortunately, many organizations just log customer complains/compliments/requests in a standard CRM program. They are not opening up the data to the rest of the organization, let alone combine it with other data sources for more insights.

So we should use the big data technology to make more informative and analytics based decisions in the existing competitive market to harness more benefits.