The customer analytics journey: move up the maturity curve

Explore the most common challenges that businesses face when it comes to customer analytics... and learn how best to solve them.
18 January 2022
Vidyut Vashi

Partner, Analytics Practice

Phil Tedesco

VP, Growth & Strategy, Analytics Practice

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Customer analytics lets you take your customer data, turn it into insights, and then use those insights to acquire, serve, and retain customers. Used correctly, customer analytics can help small companies compete effectively with larger competitors and can help industry leaders achieve their goals (experts estimate that Netflix saves $1 billion a year with its customer-centric recommendation engine).

The customer analytics process is a journey. In order to derive the maximum benefits from customer analytics, organizations need to understand their level of maturity regarding analytics, complete each step thoroughly, then build on each step to move up the maturity curve carefully and intentionally.

As you will see, companies that effectively advance their maturity recognize the importance of utilizing their most relevant data, selecting the most appropriate use cases, taking action, and continuously refining their process.

The challenges facing businesses on the customer analytics journey – and how to solve them

1. Dealing with data

Data is the bedrock of customer analytics, and also the core challenge. You need easy access to high-quality data in one place – a single source of customer truth – to serve as your foundation for customer analytics.


Creating a 360-degree view

Most companies struggle to take the data that they capture and synthesize it into a 360-degree view of their customers. In fact, ensuring data quality and access from a variety of sources are the top two concerns for analytics and measurement professionals (source: Forrester / Burtch Works Q3 2019 Global State of Customer Analytics Survey). On average, poor data quality costs businesses approximately $10 million to $14 million each year, according to research firm Gartner – in part because the vast majority of the data that companies generate is siloed and/or unstructured. Even once you do have the data, proper data stitching requires significant resources and expertise.

Trying to build instead of buy

Despite the fact that access to high-quality data is a top concern, too many companies try to take on this daunting challenge themselves. Unfortunately, most organizations simply do not have the in-house expertise and industry-wide perspective to perform the high-level data engineering that leads to optimum results. Companies should strongly consider whether their internal resources are truly qualified (and available) to build and maintain a sustainable process for ingesting, enriching, harmonizing, and visualizing data.


Start with the data you have

You don’t need the full 360-degree view to get started. Think about data on a continuum. If the data you have is mostly incomplete, you may not want to rely on it, especially for highly strategic business decisions. However, if your data is fairly complete, then use it as a starting point and plan to build on it. Consider what data you need to achieve your goals; for example, if you’re focused on increasing the lifetime value of current customers, you may not need data about prospective customers.

Bring in the experts

Customer analytics service providers with data engineering expertise typically have a deep understanding of your industry, which means they understand what analytics models will be most effective, and can focus on the most useful data right from the start. Experts can be a costly up-front investment, but also allow you to get to answers faster and more efficiently, which typically provides significant ROI (for example, the cost to build a customer record correctly up front is approximately one-tenth the cost of rebuilding an incorrect record). Some organizations choose to take a hybrid approach, using a data engineering vendor to build the data set that is then maintained by an internal team.

Align people, processes, and goals

Having clarity right from the start is key, which is why it’s important to agree on specific customer-centric objectives, and how the right data will help you achieve those goals. For example, if you want to increase customer retention, what data will you need to score every customer on their likelihood of being retained? Do you already have this data, or can it be easily obtained? Once you have the retention scores, what action can you take? Does everyone on your team agree? Spend some time thinking through the process and gaining alignment, and recognize that you can’t achieve everything at once.  

2. Developing an analytics use case

Many companies have a wealth of data, but a dearth of insight about their customers. Determining how you will use the data to build use cases is the key to discovering opportunities and building profitable customer relationships.


Differentiating the customer experience

Determining what each customer wants, how to manage the customer relationship, and how to get the most value out of them requires personalization. Simply having clean, high-quality data doesn’t make this data useful. Segmenting your customer base is a start, but will not typically give you the detail required to maximize your ROI. Customers today have too many choices, which is why you need a personalized model to speak directly to them and get them to take action.

Staying focused

Many companies try to do too much at once, and lose sight of their most critical business objectives and customer objectives. It’s smart to plan ahead, but beware of the temptation to start designing too many use cases while you’re still in the data engineering phase, which can lead to missed opportunities and extra work.

Identifying the right model for your business’s maturity level

Many organizations get caught up in flashy tools like artificial intelligence, try to be too aggressive with their goals, or don’t adequately account for different levels of data literacy between units. Be honest about your capabilities, and be wary of supposed shortcuts such as pre-packaged software, which may seem like a good start, but is not designed to help you develop a sophisticated model tailored for your unique business.


Pick a use case that will deliver financial ROI

How do you decide which model to use when you have multiple choices, many of which could likely deliver results? Look for the one that is most likely to make your company a meaningful amount of money, which will validate your efforts and improve buy-in. Consider starting with up-selling, cross-selling, and retaining your existing customers rather than acquiring new ones (retention is up to 95% more profitable than acquisition, according to research from Bain & Company, in part because you already have the data).

Test and learn

Customer analytics is a process. Do controlled experiments to test and learn, then repeat the cycle. Once you determine the uplift from a specific model, you can measure the ROI and look for ways to improve it in other models. The companies that are experts at leveraging customer analytics are the ones that are never satisfied with their results. A good model is simply a starting point for a great model. An experienced customer analytics service provider can guide you through this process, with work plans and checklists to help you continuously improve your results.

3. Transforming insights into relevant business actions

Once you’ve put the effort into your data engineering and creating use cases, it’s time to extract all of that value. This is the last mile, where you determine the best course of action for maximum positive impact on your business.


Understanding that a model is not a decision or an action

Organizations often lose sight of the fact that they need to take action. They spend all of their efforts on getting approval for a customer analytics project or dissecting the data. But a customer analytics model is simply a synthesis of your data. You need the right people – and the right processes – to turn these insights into action.

Being open to new discoveries

Many companies are not ready for insights that reveal something entirely new about their customers. They’re looking for models that confirm and validate what they’re already doing. Organizations that have a mature grasp of customer analytics, however, recognize that they may need to incorporate operational changes, and modify how they interact with customers.

Getting buy-in from business stakeholders

If there is a lack of data literacy within an organization, you’ll struggle to have people see the value of measurement in analytics and execute the plan. Gaining buy-in from senior executives and stakeholders throughout the company is key, especially since so many departments (e.g., marketing, IT, merchandising, etc.) must all work closely together.  


Collaborate from the start

Mature companies have all stakeholders aligned from the very beginning. Getting buy-in from the start of a customer analytics project means that everyone agrees upon the current situation and the end objectives. When you understand where your customers are – and you clearly define your goals – you build confidence and trust with your team, which helps you overcome any resistance to change and deliver results.

Choose a service provider with advanced execution capabilities

These days, many customer analytics models are commodities, so consider how service providers differ in terms of their execution capabilities and last-mile solutions. Customers expect greater levels of personalization, which means you need a partner with advanced execution capabilities to improve your effectiveness. The best providers can help you scale across your enterprise when the time is right for you. Their experience in last-mile execution and transforming insights into action will allow everyone to make the most informed decisions.

Have the analytics enablers involved early on

Organizations that are highly successful at using customer analytics invite the analytics enablers to the table from the start, so they can gain a thorough understanding of the business operations and goals before they even touch the data, and determine the best place to seamlessly embed an analytical output. Including these experts also allows them to guide others through the analytics process by identifying appropriate models, explaining the outputs, and discussing the potential impact.

Close the loop, learn, and refine

Customer analytics is a never-ending journey. Mature organizations study the effectiveness of their campaigns, continuously refine them based on the data and facts that emerge, then retrain the model and keep repeating this entire process. These organizations are already performing customer analytics at a high level, but are always looking for better tools and processes to improve their results. This disciplined approach, experimental mindset, and eagerness to explore new opportunities are hallmarks of successful companies.

Need expert help?

Kantar’s customer analytics solutions help you maximize the value of each and every customer relationship by blending cutting-edge analytics and deep human understanding powered by broad technology expertise.

Please get in touch to learn how we can help you succeed in your customer analytics journey.

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