Customer analytics is a process that transforms data into predictive insights to increase customer loyalty and advocacy, retain consumers at danger of leaving, and recruit new customers while increasing lifetime value. Capturing, analyzing, and acting on data to better understand your consumers might seem daunting, yet 90% of the world’s data has just been produced in the previous five years.
Nevertheless, thanks to recent technological developments, big and small businesses may now gather and analyze the data they already must create accurate forecasts about client behavior. Therefore, customer analytics is no longer the intimidating idea it once was. And customer analytics solutions are no longer a nice to have for companies searching for a competitive edge. It’s a must.
Steps to Build a Successful Customer Analytics Strategy
Defining Your Objectives
When requesting information about your consumer, start with the end in mind. Concentrate on the information you require to accomplish your goal and establish clarity. Ask as many questions as possible about your plan after you’ve decided what it is.
For instance, if you want to use a cross-sell strategy to increase your client’s sales income, you should ask the following questions:
- Whom should we cross-sell to increase sales?
- What goods do they purchase from us?
- They are buying from us when? On weekdays or the weekends, throughout the day, or at lunch?
- How do people purchase our goods? Is it online or off?
- Which retailer, if any, do they use to purchase our goods?
- What means of communication do they employ with us?
- What is their tendency for purchasing or reacting to advertisements?
- Why do they buy the goods? How do they make use of the goods?
These questions might assist in justifying a project and specify its parameters or restrictions.
Monitor the Metrics
Once your data has been collected, you need to store it and use it to monitor important consumer analytics KPIs. The customer analytics metrics aid the company in evaluating its performance and determining if it is on the right track to achieving its strategic objectives.
Key performance metrics may now be successfully tracked via real-time dashboards. These dashboards assist the company in making crucial decisions quickly, boosting clientele and income.
Review the Data
Visualizing your data is the first step toward analysis. Understanding the results of your marketing and business plan is made simple by data visualizations. The analyst may find outliers and trends in the customer data by using data visualization, which enables them to choose the best data analytics methods for analysis and modeling.
Once the client data has been explored, cleaned, and prepared for analysis and segment comparisons, the analyst can proceed. The analyst can then decide whether to undertake predictive or categorization modeling based on the business aim. Typically, the analyst will investigate several models before deciding on the top model based on model outcomes.
Review the Model
Metrics including the accuracy level, recall, precision, and the area under the curve are generated from the confusion matrix to assess how well the model performs on unobserved data. This is a crucial stage since it is quite possible that the model coefficients are not all acceptable if the model checks are not carried out.
If the improper model coefficients are used, clients will be placed in the wrong customer profile groups and may be targeted with the wrong items or through the wrong communication channel. Misclassification of clients or goods may be expensive; thus, it’s best to keep it to a minimum.
When models are profitable, and revenues are increasing, it’s time to automate your system. You can do so by fusing client information, operational procedures, analytical methods, and models with the corporate plan. The advantages of automating your customer analytics system include setting up triggers that notify the analyst when undesirable alterations in the model’s accuracy occur.
Since promotional expenditures and the overall company plan would need to be altered given the decreased model accuracy, business stakeholders must be informed of changes in the model accuracy.
Key Aspects of Customer Analytics Services
Customer analytics refers to a broad range of components that analyze different facets of customer behavior. Here are the three points that should be considered when establishing consumer analytics for your company.
Customer behavior analysis aids in the development of effective marketing and sales strategies that are adapted to the individual demands of each customer. By concentrating on the right demographic, marketing expenses may be significantly reduced, and personalized marketing campaigns can increase conversion rates.
You may start creating customer retention plans to proactively engage at-risk consumers to prevent future churn by analyzing the “why” behind customer attrition. The objective is to provide individualized customer care and assistance to earn their trust and encourage additional engagement.
You may also include a customer knowledge base that serves as a self-service tool and enables users to access information about your goods and services and rapid solutions to their inquiries. The more familiar they are with your company, the more inclined they are to buy from you (again).
A helpful and individualized experience is essential for a successful consumer relationship. Customers value customized solutions that are made to meet their unique requirements. You can communicate more successfully by learning about their challenges and assistance needs. Customer experience metrics like NPS, CSAT, etc., are very helpful in increasing product adoption. It also helps in cultivating a good relationship with your present clientele.
Customer Experience Analytics Use Cases
Today, many businesses utilize customer analytics to enhance customer interactions, attract new clients, keep existing ones, improve their supply chains, and reduce business risks. Here is one of the best cases of businesses leveraging consumer analytics for profit.
Every time a user signs in, they will find a ton of content that piques their interest since Netflix is adept at reading the audience’s pulse. Netflix collects information from users from all walks of life and has more than 130 million subscribers worldwide. It uses big data analytics to identify user search and viewing behavior trends and then recommends what they should watch next based on those findings. It goes further.
Netflix collects information on a variety of topics. One instance is that it is aware of the series you are viewing, how long you plan to watch it, and when you will go on to something else. Netflix will opt to release another season of a canceled program if 70% or more of users who started watching finished all seasons. This data indicates a higher likelihood that viewers will also watch the upcoming season.
Netflix keeps its users or members attached to its platform by providing data-backed, engaging, tailored content selections.
Keep in mind that your customers are your true assets; thus, you should treat them well. If you give them precisely what they want, they will be the ones to spread positive word of mouth. And the only way to do this is by putting the proper consumer analytics strategy in place. To start leveraging your current client base to attract new consumers and keep the ones you already have, gather pertinent data from multiple sources and employ the right analytics tools and strategies above.
To learn more about how data analytics solutions can help your business grow effectively, contact SG Analytics.