Data Keeps Prices Low with The Power of Analytics

There can never be enough emphasis on how important it is to get the pricing right. It has been calculated that an increase in the price of 1 percent translates to an increase in operating profits of 8.7 percent (assuming no loss in volume, of course). However, we estimate that approximately 30 percent of the thousands of pricing decisions companies make every year do not deliver the best price to customers. That’s a lot of lost revenue.

The situation is especially troubling when you consider that the flood of data available to companies nowadays allows them to make significantly better pricing decisions. The value of data can be substantial for those who can bring order to its complexity. The number of customer touchpoints keeps growing as digitization fuels a multichannel world. Prices must keep pace as well.

It is estimated that companies that are not exploring and acting upon the opportunities presented by big data are missing out on millions of dollars. The key to maximizing profit margins is to use big data to find the best price at the product level rather than drown in a flood of numbers.

Price plays a crucial role in helping an organization to grow its revenue and determine profitability. Among many other top organizations, Amazon uses dynamic pricing based on customer demand and behavior. Certain companies, such as Uber and Careem, charge higher prices during peak hours. These prices are based on an algorithm that manages supply and demand. Retail companies like Walmart are successful because they offer low prices daily to their customers, increasing sales and customer loyalty. Today, many businesses are leveraging the power of data to optimize pricing decisions with the advent of big data technologies. An increase of even 1% in price can improve 10% the operating profit of an organization.

Marketing professionals often ignore these issues. In the face of such problems, they tend to revert to outdated methods of managing the products or cite “market prices” as an excuse for failing to take any action. Prices are set based on simple factors such as the cost to produce the product, the margin, prices for similar products, volume discounts, etc. Moreover, they use “tried and tested” historical processes, such as a universal 10 percent price increase on all products.

“This happened every year because we raised prices not based on science but scale and volume,” said the director of sales operations at a multinational energy company. “Our people were confident that it couldn’t be done any other way. Our employees weren’t well prepared to convince clients that they needed to raise prices.”

Understanding the data at a company’s fingertips is crucial to better pricing. It requires not zooming out but zooming in. Tom O’Brien described this approach as group vice president and general manager for sales and marketing at Sasol: “The sales teams knew their pricing, but this approach provided detailed information based on every invoice, by product, by the customer, and by packaging.”

The most innovative ways to use big data in a B2B context often go beyond pricing and address other aspects of a business’s commercial engine. Dynamic deal scoring, for example, gives price guidance based on sets of similar wins/losses, decision-escalation points, incentives, and more. Technology companies have used this approach with great success-resulting in return on sales increases of four to eight percent (compared to the same-company control groups).

Since the factors that drive anyone’s deal will vary, using an overarching set of values as a benchmark will be useless. Using smaller, relevant deal samples is necessary. Companies need to do four things to get sufficiently granular. In order for an organization to grow, it is important that its pricing strategy plays a key role since it directly influences the organization’s four main objectives:

Companies already have a treasure trove of data at their disposal, so setting the best prices isn’t a data challenge; it’s an analysis challenge. B2B companies tend to manage data rather than use it for decision-making. While B2C companies take advantage of the wealth of data, they are not as good at interpreting and acting on it. An analysis can reveal the factors that drive prices for each customer segment and product, including the broader economic situation, product preferences, and sales representative negotiations, which companies often overlook.

Analyzing thousands of products manually is too expensive and time-consuming. Automated systems can make value decisions by identifying narrow segments, pinpointing the factors that drive value, and comparing that to historical transactional data. Data-driven pricing allows companies to set prices according to clusters of products and segments. Furthermore, automating analyses makes it much easier to replicate and tweak them, so you don’t have to start from scratch every time.

Communicating new prices is just as challenging as implementing them. Companies need to work closely with their sales reps to explain why and how the system works so customers are able to trust the prices. A clear set of communications detailing the rationale for the prices to highlight value is equally important, as is tailoring those arguments to suit the particular customer. Successful companies invest heavily into training programs to help their sales forces understand and embrace new pricing strategies.

It is also imperative for sales reps to receive intensive negotiation training to make convincing arguments for clients. To encourage sales reps to adopt the new pricing approach, the best leaders accompany them to the most challenging clients and focus on getting quick wins. The managing director of a multinational energy company emphasizes showing leadership behind this new approach. ” We accomplished this by coming together to visit difficult customers.”. Our goal was not only to support our sales reps but also to teach them the importance of argumentation.”

Companies should support their sales force with valid targets to improve performance management. Getting the right analytical skills to recognize the opportunity and maximize the front line needs to have a transparent view of profitability by customers. In addition, salespeople should have the power to adjust prices independently, rather than relying on a centralized team. Creating a customer-specific pricing strategy requires creativity and a sense of entrepreneurship. Pricing policies and performance measurements should also be changed and incentives.

We have seen companies achieve impressive results by taking advantage of big data to inform pricing decisions within a range of industries as diverse as telecommunications, software, chemicals, construction materials, and chemicals. In each case, there were enormous numbers of SKUs and transactions and a fragmented customer base; when the prices were set at much more granular levels, all of these companies saw a profit-margin boost of between 3 and 8 percent. There was a case where certain products were charged at a price that increased margins by 20% for a European building materials company. Companies must use big data to anticipate the price they should trust and invest enough resources in supporting their sales reps. Otherwise, they may find themselves paying the high cost of lost profits.