Every modern industry knows this fact; they must evolve their offerings and operations. And why must they do so? Well, they need to be more customer-centric, and for that, they must use the data available with them.
Yes, they have the data available with them. Historical operational data is the best source for building a customer-centric strategy. Yet, the process of compiling, processing, and interpreting the data into actionable insights often becomes an overwhelming task.
Operating in this modern era, you are flooded with a humungous amount of data, primarily if you run an online business. That data set varies in unit, scale, and time. It is safe to say that your business has collected both structured and unstructured data.
So, there are two case scenarios:
- your company is collecting more data than it can interpret, or
- you are struggling to analyze that data into actionable insight that can give you some business value
If you are facing any of these two challenges, fret not; you aren’t alone. This article will enable you to implement a genuinely customer-centric strategy with the help of data.
What can businesses do to create a customer-centric strategy?
Customer-centric companies are up to 60% more profitable than competitors
Big data value chain
The customer experience (CX) is the ultimate goal for companies trying to develop a customer-centric strategy. When a company successfully designs their customer-centric, it will help them make insightful steps before, during, and after a product or service is purchased.
It also helps ensure
- Your employees know their role in developing and executing those strategies
- You can operationalize the historical organizational data
- What information must be gathered for better understanding the customer preferences?
- You have up-to-date information.
Defining customer centricity
Being customer-centric means pleasing the customers during all the interactions across their path to making a purchase. Every touchpoint in the customers' journey is an opportunity for your business to provide an exceptional customer experience.
Customer-centric organizations generally collect data from multiple channels and sources to delight their customers with correct communications at the right time.
Importance of customer centricity
Customer-centric companies are 60 percent more profitable than those that don’t focus on their customers.
The benefits of being a customer-centric company are numerous, but increased profitability is the central brownie point. If you want to gain loyalty amongst your customers, personalize your offerings, make them happier. They will happily refer their products and services to their inner circle.
Data visualization is the best way to move forward as it enables businesses to
- Understand the customers’ pain points, thoughts, and needs before, during, and after purchase
- Improve services and products, as well as the channels you use to promote them
- Enhance customer experience with a customer-centric culture throughout the company
Creating an effective customer-centric strategy
By embracing customer-centricity in your business, you will create a strategy that enables the timely collection of important information about your customers. This will also allow your big data analytics engine to address the customers' needs throughout their journey.
Let us take a look at how you can create a customer-centric strategy using data
1. Choosing a CLV-focused strategy
If you embrace the fact that every customer is different, your brand will become successful. You will be able to diversify your offerings, continually evolving with their tastes and preferences.
Here, CLV (Customer Lifetime Value) plays a constructive part. It allows businesses to forecast the future purchases customers will make with your company. This forecast is done by analyzing past transactional and behavioral data using predictive analytics.
Your brand strategy needs to be based on understanding your customers' underlying needs and motivations behind their purchases. This is achieved through quantitative and qualitative data analytics of market dynamics.
2. Developing the organizational culture around customers
NPS ( net promoter score) does help your brand, but it doesn’t align with customer-centricity. As a brand, you must focus on serving your target audience. You need to focus your resources on customer-centric strategies.
You must transform your organizational structure using big data analytics and predictive analytics to know your customers' preferences. It would be best to use AI and ML to understand how to interact and when to interact with your customers. You need to be at the back of their mind, and technology can help you achieve that.
3. Identifying the most valuable customers
We mentioned earlier that it is important to treat customers differently based on their characteristics. That’s why brands must create differentiated strategies for their most valuable customers. These strategies, however, need to be tested, verified, and improved with passing the time.
Why? Well, because advanced technologies such as AI (artificial intelligence), ML (machine learning), and predictive analytics will grow more advanced with each data regarding customer behavior fed into them. And with that, you will need to tinker with your strategies to stay relevant to the competition.
4. Having better data is important than having big data
Based on the above pointers, you might be thinking that collecting more data will help you create intelligent strategies. The case is actually the opposite. As a brand, you must make sure that you are not busy collecting so much customer data that it leads to customer distrust.
For strategic marketers trying to promote brands to customers, it is essential to collect data that you can use for
- precisely forecasting future behaviors of the customers
- optimizing MVC (most valued customers) experience with your brand
You can design a customer-centric strategy with the big data value chain
When businesses implement advanced data analytics into their operational mix, they do so to look for answers
- “What do audiences want from us?”
- “What is their anticipation in the future?”
- “Is there a way for us to reduce customer churn rate?”
- “How should we make calculated bets in product feature development?”
- “What should be done to fuel future growth?”
Finding accurate answers to these complex sets of questions might seem impossible, but it can be achieved if data sets are broken out from their silos.
Your product development timeline must be based on the needs and preferences of customers.
You need to reconceptualize the data for driving product design, and then you will successfully create a customer-centric strategy.