The Role of Data Analytics in Analysing your Customer Interactions
The world we live in today almost seems like it’s powered by the internet, the web is accessible to all so much so data shows that one in three people have access to the internet. And accessing it is also so easy with every other person having a mobile phone. With millions of users using the internet simultaneously it comes as no surprise that millions of terabytes of data is being shared everyday across the world. But this vast amount of data cannot be analysed without the help of a few tools and techniques which can simplify the process for us while making our online experiences more personalised and effective.
And another problem crops up in the form of identifying the available types of data. And even if you’ve managed to accomplish this, driving good outcomes after applying it in real time is another. Try this simple yet powerful three-step framework, with these companies can describe the philosophy behind this application of data analytics to customer interactions.
In the last decade, one of the major factors that played a key role in the success of eCommerce is Data analytics, and since then it has proven its value in several other areas including biotechnology, education, health care etc. The big data analytics trend is all thanks to the increase in volume of data, especially the variety and pace of relevant data.
Whereas in the domain of customer service which should be making use of all this data to improve the field, you’ll find that the use of data analytics is unsatisfactory. The field of customer service is so low in quality because of this reason; all that valuable customer data is left unexplored and unused.
This leads to the question, why don’t companies make use of the data available? While there are several issues which cause this particular problem, the main issue seems to be a lack of integration of the different stages of the customer’s journey. This leads to a very frustrating experience for the customer because they’ll be forced to provide the same piece of information such as their account number over and over again in a short period of time. Simultaneously, the agent answering the call never gets a lot of valuable data about customer intentions or needs leading to the same problem.
So in order to erase these problems making way for improvements in customer satisfaction and cost efficiency of the call center, companies need to make better use of data analytics in typical customer service scenarios. You can personalise your customers’ journey from the moment they start browsing the site to the second they finish call with a customer service agent and tailor their experience based on their individual needs by just using the analytics available. Let’s have a look at some key requirements with which we can utilize analytics in a better way in these scenarios:
Collect data that spans the entire journey of the customer
Accumulate all the data on your customers like their profile, online behaviour etc and use this to harness the power of analytics. Even a single piece of information can prove to be crucial in converting a customer, particularly in the case of delivering effective customer service. Regrettably, when answering the customer’s call most customer service agents lack important information about the customer, like:
- Why is the customer actually calling?
- What does the customer see?
- Was the decision to call the agent influenced by events in the customer journey, if so what transpired during the journey?
- Is this a repeated problem, something other customers also have faced before? How was it solved?
You’ll find answers to most of these questions from the data about a customer. Data which is easily available and can be collected at very little cost without any necessity for any kind of infrastructure to collect it. Start with Google Analytics and it’ll put you on the right track; with just an extra line of code in your webpage (like you do with Google analytics) you will get valuable data about their behaviour, location or needs along with additional information on their social network profiles to see which other brands they like. Here’s how you can do this:
Anticipate: You’ll glean basic information by finding out who you are talking to, what task are they trying to complete, and if and when they need assistance. You can understand the consumer’s intent through their behaviour on a channel. For instance, you can find out which products a visitor is interested in through their journey on an e-retailer’s Web page. Merge the identity of the individual and their context from recent interaction history across channels with their current journey data and you can predict their intent with some substantial accuracy. The prediction can be further boosted by factoring in the location data into this. Studying all this will give you data about the customer without having to rely on the customer for information.
Apply analytics to understand your customers better and personalize the journey
Next step is to analyse the collected data, and this is crucial for understanding the customers and their needs. The most used techniques yielding the best results are segmentation (used extensively in marketing) or clustering (an approach that is very basic yet powerful when it comes to data mining); with these understanding your customer and patterns in their behaviour becomes a lot easier. For clustering, you’ll find several tools and algorithms that widely used. They can be categorised in two groups:
- Supervised methods: The algorithm is trained to categorize new data with the examples drawn from existing data. For instance: the system will use the data from a 100 examples of customers who did or did not buy the product to predict if a new customer will convert or not. When you have fairly good knowledge about the data, like how many different customer segments exist; then this approach will work well.
- Unsupervised methods: The patterns or structure in the data are identified by the algorithm without any prior knowledge. For instance: To identify the number of customer segments that should be formed and the characteristics of the customers in each segment, the algorithm will use an unsupervised clustering technique. Though the application and customization of these techniques are more complex it is still very useful in many scenarios.
Support Vector Machines, Neural Networks, Decision Trees and Bayesian techniques are some of the most popular clustering techniques which have received a lot of attention in the recent years.
Anticipate important events by using predictive analytics and machine learning
Predictive analytics symbolize more advanced and sophisticated data analytics techniques. With predictive analytics, you will have potent tools that can predict user behaviour, shift in user interests or future occurrence of particular events by extending the reach of insights into the future. From a customer service perspective, predictive analytics facilitate a number of powerful features which allow to:
- predict when a customer will need support from an agent, while they’re browsing the product website
- identify valuable prospects and reach them before they decide to call the agent
- based on the online behaviour of your customers, predict the number of customers who will call
Accurately predict consumer intent: There are several techniques one can employ to conduct predictive analysis using statistical models but in the case of customer service these models are yet undeveloped. Even in the most sophisticated type, the customers are segmented very broadly like; a particular offer might be broadcasted to people who spend more than 30 seconds on a particular page. On the other hand, machine-learning models are trained on transactional and CRM data along with data on interaction-level spanning customer touch points from Web click-stream data, complete IVR interaction logs and transcripts of chat and voice interactions with customer service agents. The predictions about current intent and context to the experience are provided by evaluating the engine models dynamically in real time, based on the customer’s historical activity and current interaction with their mobile device so that the models are wholly customized to that particular customer’s situation. Such an approach leads to a better understanding of their preferences, interests, and needs with which we can predict why they interaction with a company. It also provides insights actions based on those insights into how you can personalize the customer’s journey.
Simplify: After determining the identity of the customer and predicting their intent, utilize the data to choose the best interaction which will assist the customer to have a simple and fruitful experience. For example: choosing the right channel (or a combination of channel) through which you will engage them. Modern technology has ensured that we have a powerful tool with which we can use data (samples of our customer base to test responses) for conducting multiple experiments following a design-of-experiments framework which are also called multivariate tests. Now the best presentation methods and combination of channels can be identified and employed to engage with consumers and resolve their problems. For example: It is easier to segment visitors and treat each segment differently with the present day web world. You can understand the responses of customers to different designs quickly by showing different Web page designs to different people. And then you employ similar techniques to find out what kind of experiences drive the most effective customer experiences, thus you have reduced customer effort and more successful resolutions.
Use feedback to tune the analytics platform continuously
You never know when your customers’ behaviour and needs will change, and just like that all the assumptions you’ve made about your customers and interactions and the carefully crafted models will all become inaccurate. Of course, this has to be catered for, and most data analytics techniques allow for it. It takes continuous learning that includes both implicit and explicit feedback using real-time communications data to achieve quality predictions:
- How do your customers rate their interactions with agents?
- Do agents use the actionable recommendations, if so how?
- What action can be taken for a particular cluster of personas?
To make improvements in customer service and implement these requirements, you will need to use several tools and techniques in data analytics.
Learn: After completing all the steps, collate the results and apply them to improve subsequent interactions; this step is a key part in this framework. Use the data generated by these smart customer service applications to self-correct, improve customer targeting by learning from each interaction and predict accurately for better outcomes.
Applying the Framework: Your most basic framework will be “Anticipate-Simplify-Learn (A-S-L)”, this is as powerful as it is effective and will help you to organize the range of tools and processes at your disposal to drive relevant outcomes. Serve your customers with the help of decision making and experience engines by unifying advanced prediction and simplified experiences which will mine both unstructured and structured data on a huge scale to deliver accurate predictions.
An effective combination of platforms must do two things:
Use data models to improve assisted interactions: Next huge amounts of data must be leveraged to perform an analysis on the interactions with customer service representatives, which means 100 percent of customer interactions, including customer chats and phone calls. This provides richer data for the predictive models by learning at scale from all customer interactions, and thus increasing accuracy of those models. You can also analyze behaviour of the agent in great detail and gives scores to every single interaction on multiple dimensions like soft skills of the agent, their performance, and other factors which will give successful outcomes. The insights gained from this can be used to drive decisions on training, staffing, and agent management. And when you fuse the unstructured data with the interactions between the customer and customer service representatives along with other elements of the customer journey, you’ll be able to find any errors in intent prediction which leads to the creation of a feedback loop that is self-correcting and also helps in improving the experience of self-service applications.
An Integrated Platform: Even as companies prepare for the deluge of customer data, it is an undeniable fact that this is almost a race to take up virtual arms for even the smallest competitive advantages.
Presently, most of these tools and techniques and channel-specific and department-specific customer data repositories are fragmented. It is necessary for a customer-centric care model to be in place, especially one that can leverage this data intelligently across channels to reduce customer effort. For better customer centricity and superior, differentiated customer experience delivery, organizations should reorganize themselves around the relevant sets of technologies and processes that help them achieve this goal.
Then again, you will need different delivery mechanisms that can accelerate your transition that doesn’t need significant capital investment to cope with the required velocity of change and the relatively slow response times of typical IT cycles. You need to be ready and raring to take advantage of the big data explosion and its transformative potential for your customer care.
Multichannel customer interactions are simplified by using processes that have the technical ability in a globally integrated, cloud-based platform. Organizations can employ this kind of platform to make intelligent use of their organizational and customer data by anticipating customer intent, simplifying customer experiences, learning from each interaction; thus transforming customer care to get superior outcomes.