• 1 April 2021
  • Laura Kszczanowicz, Dawid Kamrowski
  • Big Data

Could the sales process be supported by advanced technology? Of course! No matter if you represent the B2C or B2B sector, using the proper tools and techniques you could raise your sales in a short time. It will just take from five to ten minutes for us to explain how data science can be used for your company to increase your sales. 

Businesses rely on data more now than ever before. Almost all medium and big companies deal with huge amounts of data they need to collect it, store it and analyse it to improve their work. It is very important to understand the possibilities offered by data modeling. Models can help your marketers and salespeople to improve their results easily. 

Prospecting – what is it and why is it important for your business?

The first contact of a potential customer with your company doesn’t have to end with a sale. The sales pipeline usually has 4-5 stages. A sale may not be completed in the very first moment, even if the customer shows interest in your products/services. If he (or she) meets some formulated by your company criteria (can afford your services or is in need of your product), he can be called a prospect. The prospect is a lead that is very likely to become your customer. 

From this moment the qualification stage the prospect goes through other phases and is given opportunities to learn about your company and services better in order to realize that you are offering the best product. Each business has its own sales prospecting techniques, although the goal is the same – to turn as many prospects into buying clients. Sales prospecting therefore can be called a process of creating situations in which sales can be completed. 

What is Big Data modeling? 

What does data modeling mean? It is a complex process of defining and analysing data requirements. A part of this process is establishing the structure and relations between available information. The goal of data modeling is to support business processes and enable companies to make the right, data-driven decisions. In short, data models help represent what kind of data (and in what format) is necessary for various business processes.

Data modeling techniques can be graphical the goal is to present clear specification to other members of the organisation. It is important that the model is understandable to IT specialists, managers, customers or partners.

In sales and marketing, data modeling can help experts visualize workflow and support the process of creating marketing strategy. Models allow marketers to assess the results of marketing campaigns or to explain the goals of some strategy. There are many data modeling approaches to choose from to find the ones that suit your business needs best. You can use predictive marketing analytics, for example, to identify the best targets to contact from your prospect list or decide how to apply cross-selling techniques to your customers. 

Can Big Data modeling help you create a lead generation strategy?

Nowadays, companies can gain useful information about potential customers from the web and social media. By combining data from their CRMs with information collected in real time from the web and social networks, marketing and sales specialists can benefit from predictive analysis and its products useful, business insights that can improve lead generation processes. How can deeper analysis help your company improve?

A Look-Alike Model

Your company needs to maintain old customers and find new customers to grow. Identifying potential clients is not easy though. How can you tell what kind of people will like your services or products? Try look-alike modeling. This is a process of identifying customers similar to your target groups using machine learning you’ll be able to use information about your current customers to find new ones. Use it to establish a profile of a customer who is likely to click on your ad or subscribe to your newsletter. Look-alike modeling can improve the efficiency of your marketing campaigns by helping you to reach more people who will be the most interested in the message you’d like to send them. 

Retention models

Retention models can be used on those customers that are already in your company’s system. This will help you create a strategy for communicating with existing customers and prepare them an offer by predicting if their value will change over time. Retention models allow you to develop better strategies and decide if it is better to cross-sell products or upsell particular groups of clients to new products. They can be also used to predict whether you can face a churn of customers.

Predictive models

If you decide to use your business insights to create a predictive model, you can use it to get new leads and compare them to your best customers. Then you can rank them from best to worst (targeting those most similar to your recent customers) this way you get a list of leads that your salespeople should engage first. Models can be used to learn how you should communicate with prospects when, through which channel (direct mail, email, phone, ad), using what kind of keywords. You can learn all the information you need to turn a prospect into a customer. 

Propensity To Buy model

Using this, your company can predict the purchase of a particular product or service in a predefined, future period. How does that help you? If you’re planning to start a direct marketing campaign, and you have only limited resources, you should select a subset of your target customers carefully. With the PtB model you can target those who are most likely to make a purchase after interacting with the ad. You need to remember that there are two possible situations: when you come out with an offer and when you don’t (spontaneous purchase). Data Science can help you create more efficient campaigns.

Customer Lifetime Value (LTV) model

A Customer’s transaction history is very important for any company. The LTV model uses this in order to predict the long-term potential of a customer (or the likelihood of churn). Based on this, you can decide which relationships you should focus on the most. With this model you can forecast the number of future transactions and their values, or the particular amount of the next transaction. This analysis is powered by machine learning techniques. In short, the Customer Lifetime Value model can show you how much each of you customers will be worth to you in the future so you can prioritize your business plans.

You can also use it to optimize your efforts to find new prospects. There is one possible scenario you should avoid when you pay more in order to acquire new customers than they are giving you back when purchasing. You can leverage the LTV model to answer the crucial question: “is it even worth this money?” 

Customer Acquisition model

This can be applied in multiple ways, but most of all, it allows you to identify new, unique customer groups. This model can recognize the best leads and help your marketers set up the best strategies to turn those leads into actual clients. You can reach your prospects in many ways through email, posts on social media ads, etc. With customer acquisition models you can find prospects that you couldn’t see before by engaging them earlier and getting their attention. You can use this technique to prioritize customer groups and learn what kind of products they might be interested in (better recommendation system), while automating personalized ads, content and messages. 

Data modeling can improve the efficiency of your prospecting. Contact us, if you’d like to learn useful business insights to make better, data-driven decisions.