As a 177-year-old organisation with the most credible source of business information and a data repository of over 300 million companies across the globe, D&B has been helping companies with revenue acceleration, cost reduction, risk avoidance and overall business transformation. While capturing commercial activity signals for these companies from more than 30,000 global data sources, it uses D-U-N-S Number, their proprietary unique business identifier, that helps give a unified view of the data they are dealing with. They have been helping companies improve business performance through their analytically-derived insights.
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Leading their analytics division is Omkar Pandit, who started his analytics journey with companies such as Mu Sigma and Fractal. Having explored analytical tools and techniques, he now focuses on solving business problems within functions such as marketing, supply chain etc.
Analytics India Magazine got in touch with Pandit to have a detailed interaction on how analytics and D&B go together, and his take on advanced analytics and machine learning for the organisation.
AIM: You are one of the biggest aggregators of B2B commercial information. How is the information generated from this pool of data? What do you do with this information?
OP: D&B, with its huge data repository, offers credit risk, sales and marketing and supply chain solutions. In order to understand risks and opportunities before conducting a business transaction, organisations get third-party assessments done on their business partners. It is difficult to find intricate details about a company through the internet as the credibility and authenticity of such data is questionable. Talking about complex data, we start with the foundational information such as the legal name of the company, industry, establishment year, directors and promoters and holding patterns, etc. Diving deeper, the data starts getting richer and more complex by adding financials, compliance status, etc. D&B collects such commercial information from statutory bodies, and by the virtue of our database, we also function as an independent credit opinion agency.
Use Case 1:
Let us take the example of a car manufacturer in Germany that wants to buy auto ancillary parts from a vendor in Pune. In order to do business, both the companies will require a letter of credit from their bankers, wherein both parties have to guarantee the transaction. In such a case, the bankers would come to D&B for a report on the company, in order to assess its credit ratings and financial health. This helps validate the authenticity of the companies.
Use Case 2:
Furthermore, from a sales and marketing perspective, let’s look at an example where a software company wants to target manufacturing companies in Maharashtra with employee size greater than 100 and revenue of more than INR 500 crores. What seems like a simple problem, needs tremendous data engineering. Revenue information comes from the Ministry of Corporate Affairs which has CIN number as the identifier, whereas employee information comes from the provident fund and other statutory bodies which use their own identifiers. With millions of companies in each source, this quickly becomes a mammoth data science problem. Based on the name and address, we map each company with D-U-N-S Number. This, in turn, can be used to map many different data sources.
AIM: Where does analytics fit in? What are the avenues in analytics that D&B is exploring? Would you like to highlight some of the recent work in analytics that company is venturing into?
OP: At D&B, our constant aim is to provide the best analytics solutions to our clients. We weave Analytics into our B2B marketing solutions and credit risk solutions with the help of our data repository which is the largest in the world.
Building on to the earlier example, if a software company is looking at targeting manufacturing companies, traditionally one would offer the B2B targeting solutions based on firmographic filters – industry, revenue, employee size, etc. However, our Analytics team changes the problem statement in such a case to identify the companies with “high propensity to spend” as opposed to revenue-based segmentation. Furthermore, sales acceleration in the B2B market is an expensive problem given high client acquisition costs. This is usually due to several factors such as talent costs, long sales cycles, and extremely low conversion rates. Analytics-driven targeting and prospecting is imperative for B2B companies in telecom, software, hardware, logistics, insurance, banking, etc.
Recently, we helped a major payment bank to identify the economic hotspots for merchant and customer acquisition. We leveraged our database of commercial entities using geospatial data in order to acquire low wage employees mapped to the proximity of micro and medium merchants. This information then lays the groundwork for field sales teams to acquire both merchants and customers.
AIM: How big is your analytics team? What are the skill sets you look for while hiring?
OP: Our team is a group of carefully curated AI/ML, and data science experts with superior business problem-solving skills. A good candidate should have four main qualities:
- Drive and passion for creative problem solving
- Rigour for math and statistical skills
- Ability to translate the math into algorithms and efficient programs
- And good storytelling skills.
It is not necessary that one person has all the skills. The idea is to optimise the skill levels of the team portfolio. There should always be a lot of emphasis on creative problem-solving. In a B2B environment, the transactional data is hard to come by, as opposed to B2C where rich transactional datasets are available easily. The data we typically deal with is noisy and sparse. We need skills of re-defining the problem and the use of heuristics, as other traditional machine learning approaches fail.
Once a problem is framed, strong technical skills are required for implementation. We leverage Hadoop sitting beneath SAS, R, and Python to work through the enormous amounts of data and create analytical models. It is then made presentable and consumable for the client using visualisation tools like Tableau, Power BI, etc.
AIM: How is D&B’s analytics solutions different from others?
OP: Most companies in the analytics space are either service providers or software and technology providers. D&B, on the other hand, is a content provider along with deep B2B domain expertise. Let’s take the example of a consulting firm. The firm essentially has talented people with in-depth knowledge on how to solve a client’s problem. They go to the client and ask for their internal data in order to work on the problem. Their business model is talent arbitrage. However, at D&B, we bring our own data to the table and use it to solve a problem by fusing our data with the client’s data. This helps to derive richer and more actionable insights.
We have a program called Trade Exchange Program in which participating organisations share the payment patterns of their customers with us. With this proprietary information, we help clients improve their credit process by identifying which of their customers do not intend to pay on time. To elaborate further – if a customer is not paying one vendor on time but making timely payments to other vendors (who happen to be our program participants), it may imply an intent issue. To conclude, we bring rich content and the capability of coupling that with the client’s internal data to solve business problems.
AIM: How has the growth story been so far in India?
OP: We have tasted early success with Analytics, especially in telecom, finance and IT companies. The focus is on increasing the quality of discussions by changing the dialogue from data to actionable insights. We want to shape how data flows within an organisation and the decisions it impacts. The idea is to constantly be on the lookout for more creative use cases where our content can be leveraged to solve wider problems for our clients.
AIM: What kind of challenges are customers today looking to overcome with analytics solutions?
OP: Consolidation of information is one of the biggest challenges in India. Most organisations grapple with leveraging internal data systems. With the NBFC crisis at hand, there is a massive need to implement the RBI mandate for Early Warning Systems. PSU Leaders in the banking sector are working on consolidation and getting a unified view of the financial health of its borrowers from information that they gather through stock audits, follow up financial reports, etc. On top of this information, they are required to overlay an external view from multiple sources such as CIBIL, CRILC, Tax Regulatory bodies, etc. We see significant movement in financial institutions to get their data foundation ready for the next wave of growth in order to cater to the underserved MSME market.
On the sales and marketing front, most companies that we currently working with, such as telecom and IT, are seeking to improve their CRM systems through a unified view of data, and enriching their information with analytics infused data elements. This would help them with better segmentation and prospecting, which in turn will enhance their customer lifetime value. This unified view improves alignment between Sales and Marketing functions and serves as a single source of truth.