The Power of Data for Sales Businesses

Analytics plays an increasingly important role in B2B sales while and high-performing sales organizations take it to a new level to differentiate themselves from their competitors.

A recent survey gathered the answers from more than 1,000 sales organizations around the world and found that more than a half (53%) of those “high performing” ones rate themselves as effective users of analytics.

Yet despite all its tangible benefits for sales, many companies still struggle to benefit from even basic analytics, while some have not even started to work with the data at all.

But how do well-designed analytics programs deliver significant top-line and margin growth? They simply guide sales teams to better decisions. But that only happens when companies follow the two main things well: focus on areas where analytics is the most important and implement it wisely.

The quantity of data available about so many consumers shopping online enables retailers to better understand their customers, competitors and future trends.

The new powers provided by data can be leveraged by businesses to better analyze competition’s inner working, to see customers as individuals, meet the latest opportunities for greater efficiency throughout the supply chain, and to see how effective their marketing strategies are.

Data analytics use cases in sales

1. Improving lead generation

Analytics is used to improve the accuracy of lead generation and automate presales processes, filtering rich data sets to identify the right customer in the right place at the right time. This data includes historical market information, internal data, granular data sets on each prospect, previous sales strategies, their results, and more.

The right algorithms can then predict which factors truly matter in lead conversion and guide sales strategy accordingly.

Furthermore, several companies are experimenting with AI-enabled agents, leveraging predictive analytics and natural-language processing to automate early lead-generation activities. Among such activities: answering basic customer questions and automating initial presales processes.

Aside from being much more efficient than traditional approaches, these algorithms can identify the most promising prospects and define the most opportune time to target them.

2. Identifying strong and weak points

As a wholesaler or distributor, you will have lots of products to sell, but understanding which ones are strong and analyzing product sales trends is a great way to optimize your efforts.

Maybe you’ll find that, due to a big competition, a product’s profit margin is inconsistent or the quantities you’re selling should be changed. Knowing which products are on the up or quickly declining will allow you to adjust your sales strategy in accordance, and, therefore, have bigger profits.

3. Achieve better segmentation

Knowing your customer (when they visited your online shop, when they last bought any of your products or when they last contacted your support team) is vital for ensuring customer retention.

Using data analytics, you can segment your customers by engagement level and prioritize those that haven’t been contacted for a while or any that are at risk of losing interest.

The more your sales data is filtered and analyzed, the better information you’ll have for your prospects and customers, the better you will segment your data into useful action points.

For example, you could create a marketing campaign that targets all the customers who have bought water filters from you in the last 2 months but not any filter cartridges, based on their location and where stock levels are high for your products. Since such products go hand-in-hand, you could create a special offer for anyone who buys both.

4. Optimizing your pricing structure

Pricing your products competitively can be tricky, but it is one of the most important parts of your sales. Recently, one well-known software company was able to increase return on sales by more than 20 percent by simply providing pricing information based on statistically similar deals to the field. Similarly, a software firm tested more than 20 different combinations of price and value propositions and found out that to maximize revenue it needed to raise their prices. Although this move cut the number of potential sales by 10 percent, the average size of each sale grew by 25 percent, leading to an overall increase in revenue.

Deal analytics can provide price transparency and allow sellers to make complex trade-offs during negotiations. For example, B2B sellers have always relied heavily on experience to guide their pricing decisions, but in the last years purchasing teams started to implement their own sophisticated pricing tools, which led to putting sales teams on the back foot.

Data analysis allows you to see key financials for each product line using the data about GP, costs, revenue, and quantities, in this way helping to define the best value price for both you and your customers. You can also track the price changes and see how it influences your bottom line. Based on your data analysis you’ll be able to automate such tasks and make your selling life easier.

5. Maximizing customer lifetime value

Many companies that have complex product portfolios can find it tough to match their solutions to specific customer needs, while using simple decision rules still requires time-consuming interactions and often leads to missed opportunities (like those of selling related items).

Nowadays, many B2B companies prefer next-product-to-buy algorithms that draw on data about what similar customers have bought. This approach uses data and works perfectly.

Furthermore, there have been lots of historical examples when by simply identifying underserved customers, the companies boosted revenues five-ten-fold for their pilot products. The approach also helps retain customers. For example, engaging customers at risk of leaving for a competitor requires recognizing the first signs of customer discontent well in advance they stop buying. These types of problems can be fully delegated to pattern-recognition skills of machine-learning algorithms.

6. Risk management

Today’s risky business environment calls for new risk management tools and techniques. Regardless of the sector, businesses need to have a proper and up-to-date plan in place. Foreseeing risks and mitigating them – are critical skills for companies that want to remain profitable over time.

The tools available in data analytics allow businesses to quantify and model the risks in an automatic way, and thus enabling them to enhance the quality of risk management models. Furthermore, such tools are able of processing huge amounts of historical data, filtering, selecting the most valuable pieces of it, drawing insights, and changing sales strategies accordingly. So, the organizations that rely on data collection and analysis can solve complex and dynamic problems in a short period of time.

Wrapping up, data analysis techniques can introduce insight, big changes, and efficiency propelling your business to new heights.

Designing and deploying analytics requires getting five elements right.

• Internal and external data sources - Over time, data quality improves as positive early results justify greater investment in data infrastructure and quality.
• Analytics talent and expertise - This means hiring people with advanced skills in statistics and/or machine learning but also experienced sales-analytics experts who can translate the insights into actions.
• Analytics tools and advanced technologies - It is true that many leading solutions are relatively inexpensive and can be used from the cloud. But further investment is recommended in the future, especially in data infrastructure.
• Sales workflows - Which includes both human action&decision and automated digital action or event.
• Change management - That consists of the proper communication, incentives, and training.

In many industries, it is the adoption of advanced analytics tools that has begun to differentiate the winners from the rest in their fields.

However, it is important not to get caught up in endless circles of analysis. Companies are most successful when they focus on extracting the full value from a couple of use cases rather than trying to implement a broad-based analytics transformation right away.

Quick analysis done by a good professional will often surface the best options to start with, then additional work may be necessary to improve trickier fields like scaling and security.

Contact Utah Tech Labs to start or learn more.