Using Data to Unlock the Potential of an SME and Mid-Corporate Franchise

By mining huge reserves of customer data, banking analytics leaders are meeting the needs of hundreds of thousands of customers, with new levels of convenience and cost efficiency.

SMEBROctober 28, 22:07
Using data to unlock the potential of an SME and mid-corporate franchise

Banks have long pondered the untapped value of the commercial segment but often lack the means to identify the precise needs of individual companies in this large and diverse population. This is changing, however. By mining huge reserves of customer data, banking analytics leaders are meeting the needs of hundreds of thousands of commercial customers—from small businesses to medium-size corporations—with new levels of convenience and cost efficiency. Several banks have achieved a ten-fold increase in the success rate of product recommendations, thus delivering highly relevant offers with clear economic benefit. This article highlights recent examples of how “next-product-to-buy” (NPtB) recommendation engines are identifying time-critical needs for their small- and medium-size enterprise (SME) and mid-corporate clients.

The problem: Most businesses are “invisible”

Many banks currently use rules-based models to generate recommendations for SMEs and mid-corporate companies (with annual sales up to $100 million), but with limited success. Relationship managers often view these recommendations with skepticism, as conversion rates typically range between three and five percent. They resort to general propositions designed for the consumer segment and devote most of their energy to those clients whose businesses they already know well and whose needs they can anticipate reliably. The result is that 25 percent of a bank’s commercial customers usually account for 85 percent of the revenues, and the remaining 75 percent represents the “long tail” of untapped potential. These companies are effectively invisible to the bank’s sales force.

The solution: Anticipate the client’s next step

Banks are investing in building up predictive models globally: US Bank and TD Bank in North America; Itau and Banco do Brasil in Latin America; Barclays Bank and Lloyds Bank in the UK; ING, Banco Santander, and BBVA in Europe are just some examples of banks improving their commercial performance by leveraging machine learning. These advanced techniques have proven effective in diverse customer segments, from self-employed individuals to large corporate customers. SMEs and mid-corps are the sweet-spot for NPtB, as they generate massive amounts of data, which are typically underused. With the help of advanced analytics decisioning engines, banks have demonstrated that it is now practical to mine vast (and often messy) amounts of data, separating signal from noise, to arrive at precise recommendations for a client’s next action. In addition, by broadening the types of data collected for the commercial segment, banks are also analyzing customer behaviors, transactions, and customer preferences across more extensive databases.

Successful implementation of NPtB engines has boosted new sales upwards of 30 percent and increased commercial segment revenues by between two to three percent. The impact on sales efficiency has been radical in some cases, with an increase of more than 50 percent in the number of leads offered per client and as many as six out of ten customers purchasing a new product in response to a sales call.

Leveraging data for NPTB recommendations

More than a decade ago, Amazon and Netflix began leveraging data and analytics to improve their cross-selling efforts. They started with simple analytics, dividing huge customer populations into several dozens of microsegments according to key behaviors (inputs). In order to achieve this new level of precision, they used singular value decomposition (SVD) to classify customers according to patterns in their purchase histories, each pattern culminating in a target output, that is, the “next product to buy.” The number of inputs and the complexity of the algorithms used to analyze these inputs have been increasing in recent years, achieving outputs that have much greater precision than was possible with next-best-action (NBA) models. (See sidebar “From NBA to NPtB” for a summary of the evolution of NPtB from NBA.)

The NBA engines employed by Amazon (with more than 300 million customers reported in 2016) and Netflix (125 million subscribers reported in the first quarter of 2018) are not entirely suited to banks serving SMEs and large corporations with products/services that address a relatively narrow range of business activity—domestic and cross-border payments, financing, documentary credit, investments, and insurance. By building NBA recommendation engines designed specifically for transaction banking, banks have increased service levels and profitability, improving their responsiveness to SMEs and helping large corporate clients cut through complex banking relationships and account structures to optimize liquidity.

To maximize the impact of each recommendation, decision engines should identify both customer needs and the preferred channel(s) for delivering the proposal and related communication. In some markets, companies tend to rely more heavily on direct communication with relationship managers, who play a key role in following up on recommendations. In other markets, such as the Nordics and the UK, the digital channel is the primary means both for alerting a customer to a recommended action and for delivering more detailed information about the opportunity.

Consolidate data for analysis in three waves

The data reserves required to power an NPtB engine are consolidated in three waves. As the volume and complexity of data increase across the three waves, analytical algorithms become progressively more sophisticated and accurate in predicting precise, time-critical needs of individual customers.

The first wave starts with the aggregation and analysis of internal structured data of various formats, including customer demographics, product usage, profitability, and transaction history. For example, one bank in Europe started by consolidating the information it had for 1.3 million SME customers, ranging from beauty salons, doctor’s offices, and family-owned stores to small manufacturing companies and technology start-ups. This data set yielded 1,200 variables for analysis.

Continuing the focus on internal data, the second wave introduces algorithms capable of digesting unstructured data (e.g., call records, email communication), as well as a broader range of structured data from CRM systems (e.g., share of wallet, historical risk scoring, maturities, customer relationship lifecycle, company value chain, and suppliers). Fast-evolving algorithms augment the value of data already at hand by learning to recognize unanticipated clusters and associations in increasingly complex data sets. The algorithms generate actionable insights into a company’s current needs, from payables management to financing for new equipment, based on information coming from transactions and payments along the customer value chain.

The third wave analyzes a broad range of data from point-of-sale transactions to industry news and comments on social media to generate ever more precise recommendations. As machine learning algorithms become more sophisticated, it is possible to produce recommendations from increasingly diverse types of unstructured data (including, voice, image, and video files) extracted from industry and company websites, as well as news and social media.

This information was extracted from Mckinsey’s official website.