The world of today is driven by data. The switch to an online business model has pulled the rug from underneath the legs of many former brick-and-mortar enterprises and resources like customer journey analytics have begun to prop up answering the newly created demand for data solutions to increase profitability and customer satisfaction.
However, without being analyzed, that valuable data just sits around without having any positive effect on how a company is run. Big data analytics may be seen as an extremely daunting task in the minds of many, but it doesn’t have to be. After all, good use of data can drive your business into a new era of efficiency!
What is data analytics?
Data analytics is the scientific gathering and study of information. Data analytics tools and a series of techniques and processes are used by businesses of all sizes to review and analyze data in order to guide decision-making and improve the customer experience.
What is customer experience analytics (CX Analytics)?
CX Analytics is a method of customer experience analytics that takes into consideration every single interaction between a business and its customers. Analysis of constant feedback can help a business improve customer experience and brand loyalty.
Why is data analytics essential to the customer experience?
By gathering large volumes of data and condensing them into easy-to-understand informational statements one can have a clear view of the patterns that affect a businesses’ profitability, efficiency, and brand loyalty, while also learning better ways to satisfy consumer demands.
Connecting With Customers
There are many reasons why outdated techniques hinder sales. Legacy systems, data silos, outdated data, and many other possibly unforeseen problems can be harmful to sales, and service teams will not be able to fix these problems without proper access to data. Correctly managed data can make the difference between customers being nagged constantly by useless reminders to them having a positive feeling when they see your brand.
Spotting Trends
Identifying customer behavior and spotting future trends is possible with data analytics. A constant stream of information telling you what is happening in different areas of your business makes it easier to plan strategies and reallocate resources, creating better opportunities to generate revenue.
Improving Customer Sentiment
A loyal customer base is indispensable to a healthy business. Through data analytics you can gather customer experience insights, giving you the chance to know more about your customers, even their birthdays and other important life events. With this information at hand, you can improve the customer experience through personalized content.
Who benefits from customer experience analytics?
Benefits to Businesses
The main goal of implementing data analytics for a business is to optimize performance. Increased sales and profit margins, marketing efficiency, customer retention, and many other perks come with using information gathered via data analytics to influence the way a company works.
Benefits to consumers
The main benefit your customers receive from your implementation of data analytics is having a more efficient platform that is ready to give them the content they are looking for. Everyone likes it when things work exactly the way they want, and through the use of analytics, you can learn what every single one of your customers wants.
Steps Involved in Analyzing Data
The steps involved in analyzing data include:
- Mapping out your data analysis strategy
- Collecting the data
- Optimizing the collected data
- Performing the data analysis process
- Visualizing your results and putting them to use
Step 1: Mapping out your data analysis strategy
The process of customer experience management via data analysis must begin by asking questions and learning more about what you are trying to achieve. Data analytics needs to have clear goals to become a useful business tool.
Think to yourself: “What use could I give to the data I collect?”. For example, data collected from user interaction with ads could let you know what percentage of people who interact with the ad end up purchasing a product or service. The collection of this data can help you learn how effective marketing via ads is in its current state, thereby giving you the chance to set goals to maintain or increase this effectiveness.
Make sure you ask yourself the right questions before you start collecting data. This will not only give you a clear goal when you start collecting, but it will also ensure you collect relevant data. Don’t leave it at “How can I improve customer experience?”, instead think more precise statements such as, “What actions do my most loyal customers perform on my platform?” or “At what hours do I have more sales?”.
Step 2: Collecting the Data
Now that you have your questions mapped out, the next step is figuring out what sort of data you need to select to answer them. It is very probable that your organization already possesses a considerable amount of data that can be of use to you in the form of annual performance reviews by HR, located in the cloud, another online source, or stored away in hard drives somewhere around the office.
It is better to have information from all kinds of sources so you can have a view of a certain issue from all possible perspectives. That means that no matter how much information you have lying around, to improve customer sentiment you are going to have to be actively involved in the gathering of customer data.
Remember that there are many ways in which you can group the data you collect, and the way you collect data will depend on the kinds of goals you have set out to achieve. Data from customer interactions, for instance, can be grouped by age, gender, geographic location, time of the interaction, and many other factors.
Step 3: Optimizing the Collected Data
The data you have collected is not usable for drawing conclusions just after being collected. There are probably flaws in the data, and its unpolished state is bound to cause misunderstandings, skewed results, etc. The process of data optimization, also known as data cleaning, is meant to amend or remove superfluous and incorrect data, as well as check that the collected data is consistent and consolidated.
This is a vital necessity for the success of data analytics, as the accuracy of the results is directly dependent on the quality of gathered data. Using a spreadsheet or other traditional forms of record-keeping to try to keep track of the amounts of data produced nowadays is just inconceivable. The best way to keep all the data you have stored is through the use of the right data analytics tools.
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Step 4: Performing the data analysis process
There are 4 main ways information can be gathered with the use of data analytics, and the one you should use will be dictated by the strategy you set out at the first step:
- Descriptive analytics: A descriptive analysis is performed by reviewing events that have occurred during a particular period. One can easily measure, for example, the number of views a video on YouTube has received over the course of a year divided by months, and see at what times of the year people looked for that information.
- Diagnostic analytics: This kind of analysis works better when trying to figure out the reasons why an event or situation took place. Even though there must be a bit of hypothesizing involved, data from various sources is required to make sense of things. One could ask, “Does the weather affect ice-cream sales?” To answer this question you need weather and ice-cream purchase data to make a minimal assessment.
- Predictive analytics: Businesses can perform a predictive analysis by studying data from previous events and using it to try to predict trends in the short term. You can predict common variants like the seasons or the time of day and can contrast that information with customer experience research to plan ahead of customer behavior.
- Prescriptive analytics: A prescriptive analysis is one that, upon its completion, will be used to dictate a new course of action or perform changes to the current one. If a business notices through data that business is slower on the weekends but speeds up on Mondays, it may wish to move around its resources to make the best out of those busy Mondays.
Step 5: Visualizing your results and putting them to use
Once the data has been properly interpreted and conclusions can be drawn from it, it is time to make your results visible and understandable. The information must be easy to comprehend as well as visually striking, giving whoever uses it a desire to put facts into action.
By using data analytics tools like DataIntell’s platform, visual graphs and other support materials can be built from the data you have stored. This simplifies the analytics process and guides informed decision-making based on numeric evidence, serviced in an easy-to-read but fully detailed presentation.
4 ways data can be used to improve CX
Here are 4 ways data can be used to improve CX:
- Track customer behavior
- Design a satisfactory data-driven customer experience
- Create a loyal customer base
- Take advantage of predictive analytics
Track customer behavior
Customer experience insights give you crucial information on how your customers interact with your platform, and the different actions they perform while connected to it. You can have direct information on the items they purchase, the time of the day they purchase them, and many other useful tidbits of data, which you can then include in your marketing strategy or use to design new upselling techniques.
Design a satisfactory data-driven customer experience
A data-driven customer experience approach can lead you to learn more about the needs of your clients. A more accurate idea of what your customers are looking for and what they are more inclined to purchase will work much better than just putting generic offers on display. You can personalize deals, offers, discounts, coupons, and other perks to optimize your sales!
Create a loyal customer base
Attracting new consumers is much more expensive and time-consuming than keeping a high customer satisfaction score. Customer satisfaction is paramount to the success of any business, and giving your clients exactly what they want is a great way to improve your customer experience. If you understand what attracts your customers to your business, then it will be easier to keep them interested.
Take advantage of predictive analytics
Predictive analytics might not be a crystal ball, but it for sure is the next best thing. By having foregoing knowledge on growing customer trends you can adapt your approach to the market accordingly. This gives you an edge on your competition and gives your customers what they want, probably even before they even know they want it.
Optimize your customer experience with DataIntell
Bad resource management or lack of proper data analytics tools often impedes a company from putting its data to good use. Thankfully, with DataIntell’s intelligent data management solutions, you can make the best out of your data.
Using DataIntell means that all of your data, whether it is stored on hard drives, networks, or the cloud, can be easily reviewed, analyzed, and managed from an easy-to-use interface. The dashboard gives you quick access to your data, and through smart algorithms you can weed out irrelevant content, giving you a consolidated data pool to base your operations upon.
