With e-commerce growing exponentially, growth-stage companies record millions of transactions each month. With this surge in data, it has become confusing and complicated for business leaders to steer away from the noise and focus on the signal. Based on a survey conducted across 120 CFOs from the e-commerce space(mid to large-sized), it was clear that their unanimous
Challenge #1 : They understood "What was wrong" in their business but Not "What needs to be done, at a granular level, to drive the business efficiently".
Challenge #2 : Their teams were generating "tons of data" but actionable insights came too little or too late.
As per a leading research organisation, only 16% of decision-makers feel they can leverage financial data for decision-making.
These problems get further exacerbated in industries that need to be highly nimble and agile to respond to constantly changing markets. The changing customer buying behaviours (for example – buying driven by sales events like Black Friday) add fuel to an already complex sourcing and distribution model.
The First Principles Approach
We need to go back to the basics of what the CFO role stands for in today's environment - a dynamic business partner who can help transform businesses by refocusing organisational energies towards improving ROI in all activities of the organisation.
What CFOs need today:
- KPI-driven view of operations: What needs to be measured and why
- Connecting the operational KPIs to "Moolah": How will we connect the dots across systems
- Visibility into transactional level details for quick action
This is where Data mining comes in. Data mining begins with first looking at data as a mine full of value that needs to be harnessed using advanced tools and technologies. The mining tools must be sophisticated enough to sift through the dust(data noise) and get to the mineral(data insights).
Data mining focuses on telling you "How you perform and Why you perform the way you do"!
Let's take the example of the most popular e-commerce success metric followed by all stakeholders today – The GMV.
Most management reporting dashboards capture this metric and report it as is. While this metric gives you a directional view of operations, your natural enquiry would be along the lines of :
- Does this metric in itself present a complete picture of the business?
- Does it tell you – WHY the GMV moved up or down?
- Why does competitors' GMV growth rate outpace you?
And most importantly, can this metric assist me in driving ACTION to improve business partnering to increase future GMV? The answer is generally - a resounding No.
To answer questions like these, as CFOs, we need to dig deeper and correlate operational aspects of our business with our financial KPI performance. For example, Mr. New and Mr. Old are young, energetic CFOs at two different lifestyle marketplaces.
Mr. Old
- Leverages an advanced reporting tool to measure and report KPIs, drive monthly meetings, and take action.
- To understand why the KPIs moved in a period – he takes updates from his subordinates on a piecemeal and on-requirement basis. He does get a good view of business performance but cannot ascertain how changes in operations affected his GMV.
Mr. New
- In addition to the advanced reporting, also has a secret weapon in his arsenal – a data consolidation and analytics tool - that can get to the root cause of KPI fluctuations in seconds and enable lightning-fast actions.
- He is not only able to review a department's performance in depth but also able to connect the dots between performance across departments affecting overall business.
"EY Tech Horizon" survey reveals that 53% of companies are making data mining and analytics their top investment priority during the next two years. Leaders in the field are forecasting 43% greater revenue growth than laggards."
How can Data Mining help me analyse performance better as a CFO?
Data mining tools can make your journey smoother and faster by helping you :
- Analyse costs and overheads at a transactional level, which was impossible to track earlier (e.g., Seller CODB): Data mining tools highlight costs not tracked earlier. For example, Seller CODB (Cost of doing business) reflects your vendors' ease of doing business on your platform. The lower the costs, the more attractive your marketplace would be for people to sell, helping you grow your product range faster than the competition. Also, onboarding the right set of sellers gives customers an enhanced value proposition. Seller CODB can be measured through Vendor NPV; on a scale of 1-5, they are asked to give their rating. Companies need to be highly conscious if they see the detractors' scores going up, which calls for an immediate RCA on what's going wrong.
- Understand Customers Better: Data mining tools incorporate intuitive models to understand your customers and their psychology better. Understanding customer segments helps businesses focus their energies on high-value clients (generally assessed using LTV or Lifetime Value) for better results. Data mining tools enable us to distinguish customers into segments like Entry, Mass, Mass Premium, Luxury, and so on. All of them cannot have the same strategy. Hence it becomes imperative when defining the strategy for Customer Acquisitions and Repeating the user base to the extent of personalisation required to keep them engaged. Customer Acquisition Cost and Life Time Value of the customer become essential metrics to be measured, and determining the ROI of the spending becomes very relevant.
- Spot opportunities and differentiate offerings: Continuing our discussion from Point 2, data mining tools help you better understand your customer's buying behaviours and create distinct customer group-focused offerings. Segmenting customers helps businesses generate a niche while attracting consumer trust in your domain. Brands like Instagram and Ola Auto are great examples of companies that accelerated due to business pivots driven by customer buying insights. Data mining tools can help you target the right audience and can be a game-changer when marketing or advertising your brand.
- Match expectations to capabilities: A data mining strategy brings together operational data (speed of delivery, first-time-right delivery%, returns%, etc.) and its financial impact creating a view on whether we can deliver what our stakeholders desire. For example, the speed of delivery delights the customer, especially in an E-commerce setup, where the performance measure can be measured based on the number of delivery in a day, two days, and so on. The lower the number of days taken for delivery, the higher the chance of customers placing more orders basis experience requiring robust Supply chain management wherein a mix of own warehouse and Third-party Logistics services becomes very important.
- Democratise Data and Decisions: Cloud-based data mining tools and strategies place power in the hands of the first responders. Cloud-based analytics has a two-fold benefit – Decreasing the time to respond to challenges while creating a sense of ownership across the organisation.
These tools bring about immense transparency, creating a single view of organisational performance across levels and roles enabling the organisation to take action quickly and effectively, eliminating any potential bottlenecks or roadblocks.
The question for business leaders now is this – Do you see yourself in the shoes of Mr. Old or Mr. New.
In one the studies by Big4, 93% of companies indicated that they plan to continue increasing investments in data mining and analytics.
Are you ready to embark on your journey?
