Customer analytics is an important part of any business. Data, information and insights from your customers are essential to understand what they want, what their expectations are and how you can improve the customer experience. In this blog post we will talk about tools and platforms required for analysing the data for a better customer experience.
Data is the new oil, and it’s vital for strengthening strategic planning. Leveraging data gives a competitive edge to companies from every industry! But do we know what all this data can be used for?
There are many interpretations about the various phases of the data life-cycle but a typical data life-cycle looks like this.
Data –> Information –> Insights –> Impact
Data: Data is raw information, which can be in the form of numbers or text and it comes from various sources.
Information: Information helps to identify data patterns. For instance when you are analyzing a customer database, it will show what percentage of your customers buy product X.
Insights: Insights help answer specific questions
Impact: Measures the result of any actions
Customer Analytics is a combination of activities which includes Collection of data, standardizing the date to make it uniform and easy to read, slicing and dicing of the data by using advanced statistical or tech tools to get some meaningful insights out of that and then finally using that data to see the impact.
The most essential part of Customer Analytics is Descriptive Analytics which means interpreting the customer data by looking at the trends and pattern to assess what’s working or what are the opportunity areas where the business should focus on.
Businesses use Data Visualization Tools to help them look at the data and identify patterns or trends. The most common tools are Tableau, Looker, PowerBI etc which helps the business in understanding what is happening with their customers based on various metrics like conversion rate or bounce rates of an e-commerce website.
Descriptive analytics mainly has 3 quantifiable components:
- Who your customers are, where they are from and what is their profile.
- What kind of transactions or engagements they have with your brand and what is their spend pattern or buying behavior.
- When do they engage or transact with you, are they frequent or come and go.
This descriptive analytics acts as a stepping stone for the predictive and prescriptive analytics ( forecasting basis the historical data) and helps in designing the forward looking strategy to match the outcome from predictive models.
The prescriptive analytics mainly helps in taking decisions based on the data insights ( what can be done to improve) and also take remedial measures as per requirement. With a lot of customer interaction happening, some might like an offer while others may not require it so we have to make sure every customer is given personal attention. This specific phase or module works as a decision maker for future course of action that needs to happen with respect to the customers’ feedbacks and reactions.
Metrics: Data Analytics uses metrics like conversion rate, bounce rates, churn rates etc., which are used by businesses for different purposes such as forecasting business performance or measuring how well marketing campaigns work or understanding customer behavior patterns better.
Once the data is in place and you know what is the end result or outcome you are chasing it’s time to leverage on the technology for transforming the heavy data into meaningful and refined clusters.
Listed below are few tech platforms which can be very useful to store heavy data and optimize the desired results.
- Customer Data platform: A great tool which enables organizations to centralize their data collection and unify customer profiles from disparate sources. It also helps in and creating and managing segments. CDP can not only store the first party data but also the second party data from brand website, mobile app and social handles.
I have deployed CDP to integrate the Social data with CRM and customized campaigns were curated based on interests and actions of customers on social platforms. This led to a 20% increase in CTR.
Data Lake: A data lake is a large repository of raw data which comes from different sources, unstructured and structured. It can be used to store massive amounts of data for future use without the need to worry about sector-specific technologies. One cannot only analyze it using SQL but also any other analytical tool like Tableau etc. The results of this analysis can be fed into thee CDP.
- Business intelligence tools– BI tools transform raw data into meaningful and useful information to enable more effective strategic, and operational insights and decision making that contribute to improving overall enterprise performance. It’s also an effective tool for reporting and data visualization.
Take an example of a financial institution with a new customer who just got a loan, with a BI tool it can analyze data about that customer like age, income, etc and the financial portfolios of similar customers to identify the opportunity for additional product marketing.
- Customer Journey mapping – These tools help you identify the most important moments in your customer’s journey which brands can fill with relevant offerings matching customer needs. An effective customer journey mapping tool will help the brand to plot the customer journey spread across three phases:
Awareness stage: buyers are able to identify the challenges that they are facing
Consideration stage: buyers are actively looking for ways to deal with the challenges that they are facing
Decision stage: buyers have settled on a solution and are only searching for the most suitable product
For example, a customer journey mapping tool will help you identify the moments of truth in your customers’ journeys where they need to make an important decision. These tools are especially useful when it comes to identifying opportunities for customized product offers and personalized marketing messages.
- Marketing automation– It’s a platform to plan, coordinate, manage and measure all the marketing campaigns, both online and offline. It is an effective tool which reduces the manual effort of creating and deploying a campaign. Most effective use case for marketing automation can be seen for the predefined events like Birthday, anniversary and achieving milestone with the brand. Marketing automation also enhances the buying process by automating the transactional communication at each stage of order tracking (dispatched, shipped and delivered etc
A well-crafted data strategy can be a major game changer for your company. Clarifying exactly where the real opportunities are and how to maximize them will keep you on top for years to come. Don’t lose sight of what’s important and stay mindful of changes in customer expectations.