How to build a data-driven strategy

These articles tell us why a data-driven strategy is good, but often fail to explain how you implement an effective data-driven strategy. Without this, they can often read like a book called “Why it’s great to be a billionaire”. Interesting, but not very helpful.

This article will help remedy that: we’ll explore how Capital Markets firms can develop a data-driven strategy in order to deeply understand their clients, tailor their services to their clients’ needs and uncover hidden opportunities and risks across their client base.

Let’s dive in.

The Building Blocks of a Data-Driven Strategy

An effective capital markets data-driven strategy has four key aspects:

• Capturing the right data: this is the foundation for everything else. Before you can implement a data-driven strategy, you need to capture data that can be used to gather meaningful insights
• Understanding your clients: once you have a strong process for capturing the right data, make sure you’re interpreting it in the right way to get a clear view of your clients

• Tailor services appropriately: once you understand your customers, you can start tailoring services to them, and ensure that they are serviced in the right way

• The bigger picture and the hidden gems: finally, learn how to leverage data to deeply understand client intent, interest and performance at a macro-level, as well as seeing hidden risks and opportunities across your client base

Capturing the Right Data

It’s obvious: data is the foundation for solid strategy. A data-driven strategy built on poor-quality data will lead to poor business decisions. The first (and most important) pillar of a data-driven strategy is capturing the right data.

That means:

1. The data is relevant
2. The data is high-quality

Knowing what kind of data to capture

Firms can fall victim to the common misconception that they should collect as much data as possible. This isn’t wrong, exactly, but it is far more important to ensure that the data you collect is relevant and strongly aligned with business outcomes: if I want to decide the best time of day to exercise, it would be pointless to capture data on the average cost of Nickelback concert tickets.

Capturing the right data involves identifying what information provides signals that help to answer core business questions. Within capital markets, this may look something like this:

Business question	Data that helps answer the question
Who are our customers?	Account / Contact
Which customers are we talking to?	Interaction
What are we talking to customers about?	Interaction
What are customers consuming?	Readership statistics
How much are we being paid?	Revenue
How much do we want to get paid?	Budget data

 

Capturing high-quality data

Good decisions rarely come out of bad information, so the next step is making sure you are collecting high-quality data. That means:

• It exists: you can’t use data to inform your decisions if you don’t have it

• It is accurate: Inaccurate data will undermine any data-driven strategy.  At best, it’s meaningless and at worst, it is misleading
• It is adequately rich: data needs to support the insights you want to gather. For example, if I want to understand client interest in a particular ticker, I need to be sure that any signals of interest (interactions, readership) for customers can be identified based on tickers. I need that level of detail

Capturing accurate and rich data can be difficult, especially when it relies on human input – often still the case with interaction data. Unfortunately, manual input is error-prone and unreliable, and if logging an interaction takes too long, busy salespeople and analysts will not bother.

So how do you make it work? First, embed the process of logging interactions directly into your business users’ workflows (e.g. directly from Outlook, via a mobile app or in the same place as a Salesperson’s call list). This eases the burden of capturing that data, enabling your teams to easily and quickly capture interactions without having to switch context.

 

Second, leverage technology to ambiently capture interaction data in the background from systems that are already part of day-to-day workflows (such as Bloomberg Chat and Microsoft Outlook). This removes the manual overhead of capturing interactions altogether, reducing the reliance of manual input. And when firms aren’t routinely capturing interactions through these tools, particularly Bloomberg Chat, revenue is lost.

 

By putting these principles and best practices at the heart of your transition to a more data-driven way of working, you’ll be able to reap those much-touted benefits using rich, high quality data that will underpin your data-driven strategy.

I’ll be back soon with the next instalment in this series where we’ll take an in-depth look at understanding your customers.

In the meantime, if you’d like to hear more about how Singletrack can help you gather the right data to inform your business decisions and smart-service your clients, get in touch now.