How To Make a Data-Driven Customer Strategy for 2020

Our guide will walk you through all the steps of creating an effective data-driven customer strategy for 2020.

Data is the word on everyone’s lips in the business community. And judging from what people are saying, it is a commodity with almost magical properties.

Data can predict customer behaviour; it can completely transform the inner workings of a business; it can convert random individuals into brand loyalists; and no matter how much of it you use, it can be replenished almost instantly.

While we believe that many people go too far in their adulation of all things data-related, we have to agree that data plays a central role in the formulation of any modern business strategy. This is especially true in the B2B sector, where customer insight plays a major role in marketing, sales and customer service.

With that being said, only about 4% of businesses make effective use of data at their disposal. This is a staggeringly low number when you take into account that data has a proven positive effect on business outcomes, including:

  • Productivity gains
  • Higher Return on Equity (ROE)
  • Higher Return on Invested Capital (ROIC)
  • Higher Return on Assets (ROA)

The reason why some businesses are not getting their money’s worth with data has less to do with the data itself, and more with their data strategy, or lack thereof. If you’re struggling with data utilisation yourself, the following guide will walk you through all the necessary steps of creating an effective data-driven customer strategy in the current year.

1. Assemble Your Team

In order to leverage data to accomplish business goals, you need to assemble a team of experts to run the operation. This is by far the most important element of your data strategy. If you don’t gather the required talent up front, you risk running into bottlenecks later, and this can jeopardise your entire customer strategy.

A typical data team should include at least one:

  • Data scientist, with an expertise in statistics, mathematics, machine learning, programming, and data visualisation,
  • Business analyst, with experience in mapping data to business outcomes, and vice versa,
  • Technician, for setting up and maintaining hardware and software for data processing.

Try to foster collaboration between these experts by holding internal meetings, as well as meetings with members of other departments, most notably sales, marketing, and customer service.

2. Set Your Goals

This is the part where most businesses start encountering problems with using data, often without noticing. Data has value only insofar you use it with a specific goal in mind. Otherwise, it will just end up taking space on your hard drive.

The problem with goal setting in this context is that companies make their goals too broad. “Achieve higher revenue” is a valid outcome of using data, but it is not really a goal that helps you decide which concrete steps to actually take.

The best way to express goals in the context of a data-driven customer strategy is to utilise key performance indicators (KPIs). One of the most widely used KPIs is customer lifetime value (CLV), which is how much your customers spend on average in their interactions with your business.

Customer Lifetime Value graph

3. Collect and Store Data

Before you can use data, you will first have to gather it and then store it in a convenient location. You also have to ensure that data travels seamlessly between each stage of this process.

The first thing to consider is where your data is coming from. There are many channels for gathering data, some of which you’re probably already using. These include website activity, email correspondence with customers, contact lists, customer purchase histories, etc.

Data Collection graphic

Data can be stored locally if you have the infrastructure for it, or you can use a cloud-based solution. Both approaches have their advantages and disadvantages. We recommend you start with a cloud-based SaaS solution, before you commit to installing specialised hardware within your office.

4. Manage and Organise Data

Data comes in many forms, some of which is readable by humans while others require specialised software to decode and use effectively. This makes data difficult to handle manually which is why businesses are using specialised automation platforms such as CRM software. CRM can help you forward data from various channels into a unified database, and it provides you a convenient interface for organising it. This makes CRM essential for effective data management.

CRM software also helps your data team manage projects, communicate and forward data to other teams such as sales and marketing.

5. Develop Customer Profiles

Between action and data there is an intermediary, often described with the term ‘actionable data’. We take actionable data to mean information on which you can act in some concrete way.

A customer profile is the basic instance of actionable data. The difference between a customer profile and the totality of data you have on a given customer is one of structure and brevity. For example, a customer that buys a lot of product in a short span of time, followed by long periods of dormancy can be shortened to ‘impulse buyer’. Impulse buyers tend to respond well to deals and promotions, which allows you to take concrete steps towards improving their customer experience.

6. Create Custom Conversion Trajectories

Data is quintessential for developing marketing tools and materials for use in conversion. This is because most data has embedded within it customer biases, content and channel preferences, and other kinds of personal information. This enables you to create custom conversion trajectories for each individual customer or customer segment.

Personalisation is the first thing to keep in mind when trying to leverage customer data. A personalised piece of advertising content has higher conversion potential than generic ads which appeal to no one in particular.

Another possibility of using data for custom conversion is through omni-channel customer journeys. Each customer has their communication channel of choice. Data allows you to find what these are, which enables you to reach out to customers where they want it, when they want it.

Data-driven customer strategy

7. Monitor, Test and Optimise

The final piece of the puzzle is data-driven optimisation. Data is not a static quantity that you simply use up. It is a constantly running stream with a certain volume. One of the consequences of this is that some data will grow obsolete over time, at which point it is overwritten by newer and more relevant data. And as data changes, so do the ways of using it effectively.

This process can be further enhanced by regular monitoring and testing. By keeping a close eye on data and testing how well it translates into business success, you will find out sooner what’s worth keeping and what should be jettisoned.

In practical terms, data allows you to create A/B testing models for your parts of your strategy. For instance, email send times, email headings, content keywords, etc. are amenable to testing, which will help you determine the most effective ones to use.

Using Data to Fuel Business Growth

Data is evolving into a currency for the digital age. By investing in data, companies can develop customer-centric strategies built on solid evidence, which directly translates into better business outcomes. Start working on your data-driven customer strategy today, and secure your competitive advantage for the future.