Building sales & marketing resilience in a recessionary environment

With the economy under pressure and the resilience of businesses being tested, sales and marketing teams stand on the frontline. They urgently need to harness data to strengthen and grow existing client relationships, and for insights into new opportunities. Understanding customer motivations is not always easy, however, and requires a sophisticated approach to data management and interpretation.

Weak growth conditions across much of Europe are testing the resilience of even the highest-performing companies. Sales and marketing teams are charged not just with piloting their businesses through the economic turbulence, but turning the situation around and acting quickly on expansion opportunities when the inevitable recovery begins.

New tools are emerging to help them achieve these goals, but as Dun & Bradstreet experts point out, there is an art to applying these solutions effectively and harnessing the power of the data that companies themselves often hold.

Business leaders also felt that when it comes to navigating the current downturn and ensuring business survival, the effective use of data is paramount.

Over three quarters (77%) of leaders globally agreed data will be vital in this regard, with some interesting variations at the national level across Europe – 68% held this view in Sweden, 70% in Germany, and almost 80% in the UK.

“The downturn has brought the topic of sales efficiency and sales effectiveness back into focus,” says Björn Gerster, Director Sales & Marketing Consulting DACH at Dun & Bradstreet Europe. “The key is to have an efficient sales process, one that shortens sales cycles and that reduces manual work through using automation and digitalisation.”

The survey showed that business leaders worldwide see identifying new customers as one of the main uses of data at their organisations, along with increasing revenue and improving customer service.

However, Gerster suggests that while it’s important to use data to qualify sales leads, when it comes to business resilience it’s equally critical to use data to analyse a company’s existing customers, in order to identify upselling and cross-selling opportunities.

He cites the example of the insurance industry, where major players have re-crafted KPIs away from new business, towards the retention and growth of the existing customer base.

Putting the ‘Know’ in Know Your Customer

Massimiliano Spennato, Senior Consultant Master Data & Marketing Solutions at Dun & Bradstreet, believes building a better understanding of customers should be “the common denominator across every industry.”

“If you don’t know your customers, you don’t know where they stand in the market, and it will be difficult to win business against your competitors,” he explains.

“The first step is always to begin at the source. First, know your customers, because then you know what activities you need to perform in order to be successful in the market, both in terms of your existing and potential customer base.”

Spennato has witnessed multiple clients implement successful strategies based on a close understanding of customer behaviour generated from advanced data analytics. In the current downturn, savvy major companies have been focusing on identifying and meeting the needs of their ultimate customers directly.

For example, one major soft drinks producer has responded by moving to deliver directly to venues, in order to increase penetration and exert more control over its market. In other industries, players have acquired competitors in order to corner strategic markets and to position themselves as the dominant supplier. Careful analysis of the right sources of data can point to precisely where such gaps and opportunities exist.

 

Making the most of your own data

So where to begin laying a foundation for effective data analysis?

According to Gerster it involves a few key steps:

• Segmenting the customer database. 
  “You need information about the sales revenues, the employees and the industry codes,” he explains. “Around industry codes, we bring in additional information because the codes alone are never sufficient in terms of building a target market. For example, website content data with keyword analysis can reveal insights on business models and customer bases.”
• Thinking in terms of corporate ecosystem. 
  Dun & Bradstreet D-U-N-S numbers are particularly valuable in this regard, not just because they represent a critical source of insight into corporate linkages, but because they allow data to be automatically refreshed and to move between sales and marketing teams without creating silos or duplicates.
  
  Linkage information “creates a kind of family tree, showing which company belongs to whom, who is the ultimate parent company, which is a standalone entity,” Gerster notes. “Our clients can build those structures into their own customer database. That’s how we approach the master data plan, and the master sales and marketing plan.”
• Structuring and integrating customer data. 
  A unified view of data not only offers the chance to do more for existing customers and strengthen relationships; it’s also key to identifying new prospects. “You need to analyse your best customers and their profiles, and identify the most relevant data points which describe them,” Gerster explains. “Then, you need to work out who in the market is similar to those best customers, so you can prioritise them in your marketing and sales activities.

You may end up with lots of profiles, but first you need to understand why your existing customer is your customer. What underpins that relationship? And for that, the essential point is that data must flow between your systems.”

”The most interesting part of this process is showing the customer what they actually can do, in conjunction with the use-cases, in terms of market analysis,” says Spennato.

“For instance, when I worked in the insurance sector, we developed a code of conduct for how we managed key accounts across the globe, based on the kind of corporate linkage insights that Dun & Bradstreet delivers.”This level of analysis is only possible, however, if the integrity of an organisation’s internal information is sufficient.

 

The importance of data quality

Productive sales analytics “starts with data quality, and you have to define data quality, which, while it’s not rocket science, needs to be better understood,” says Spennato.

This involves:

• Addressing the basic questions. For example, according to Spennato: How many customers do you need comprehensive data on? What kind of data is most relevant to your business? The answers will dictate what benchmarks need to be met.
• Ensuring data is complete, well-organised and qualified. For all the potential of new tools such as artificial intelligence (AI), Spennato notes, they yield few results unless the underlying data is in good shape, which means freed from isolated silos, with duplicates and outdated information eliminated.
• Establishing a ‘Golden Record’ – that is, a single, consistent source of truth that can be used by every part of the organisation. However the more systems are in place, the greater this challenge becomes, as relevant data points become dispersed across CRM, marketing automation and payment and credit platforms.

External providers such as Dun & Bradstreet can deliver a foundation of reliable, transparent information that provides an optimal starting point for customer-centric analysis.

“We offer a strong skeleton that can be used in conjunction with an organisation’s internal data, provided that the internal data is qualified information that can be used to predict certain things,” says Spennato.

“As a consultant, I have to bring the understanding, the transparency and the intelligence to inform my client how they can actually use this information to benefit their customer

Gerster notes that once a base level of data integrity has been achieved, internal information can be integrated with other external sources to glean insights into clients’ future behaviour – a key advantage in seizing on the opportunities that emerge in the early stages of economic recovery.

Intent data, for example, allows organisations to identify prospects that are currently at an early stage in their buying process. This leads to more targeted marketing and sales efforts, maximising return on spend.

The most sophisticated systems deploy technologies such as natural language processing and deep learning to take a keyword-based approach that identifies the location of leaders who have the authority to make purchasing decisions. All this can help ensure frontline sales teams are reaching out to and having conversations with the right people, at the right times.

In the end, Spennato notes that clients are the real experts in their industries, and that companies like Dun & Bradstreet exist to ensure they can bridge the gap between simply having data, and deploying that data to make smarter business decisions.

“One fundamental issue is that many companies still don’t see data as a methodology to solve pain points,” he says.

Given the pressure on sales and marketing organisations in the unsettled economic context, that may change. More businesses are beginning to understand how removing redundant information and low-quality leads, and highlighting zones of potential, allows sales leaders to target their efforts and work unhindered by failure-prone manual processes. When it comes to business resilience, such interventions can make all the difference.