Beyond Transactions, how Businesses can Create Lasting Customer Connections ?

Improving customer engagement is crucial for business success. Study after study has confirmed that customer interactions across Omni channels and other support functions are the biggest driver of customer satisfaction. However, most companies rely overly much on point statistics like average Handling time, FCR, etc., and they neglect the fact that customers today start their inquiry or search for support online, failing that in all the Omni channels, only to find themselves speaking, emailing, or chatting with a customer service agent. Think about that: If the customer is unable to get what they want online, and in the omni channels, then interacting with the Customer support staff is the 3rd contact that is very dissatisfying, and costly to the Business as well.

The Most Common Problems that today’s customers face are as below,

• Difficulties in Contacting Customer Support

• Delayed and Generic Response

• Lack of Transparency

• Inadequate Resolution Attempts

• No Compensation for the Inconvenience

• Lack of Empathy

• Negative Impact on Customer Loyalty

What do Customers expect before and after they buy a Business Product or service?

• Businesses know me and they remember me

• Businesses give me the choice’s

• Businesses make it easy for me

• Businesses value me

• Businesses surprise me with stuff that I can’t imagine

• Businesses help me better and we help customers do more.

Wouldn’t it be better and more satisfying for customers for Businesses to understand their needs when they reach out to any omni channel? Irrespective today many Businesses invest to improve customer engagement in various ways by way of fancy technologies, hiring more experts, and complex processes, but still, there is a lack in between what customer wants and what Businesses Provide, Some of the reasons why it’s so hard to meet the customer expectation, especially on the omnichannel basis I have observed in various places are as follows,

• All Omni channels are usually owned by different groups or functions within Business and there is a lack of tracking and ownership.

• Channel development is not coordinated well among the Business units

• All Omni Channel reporting is similarly uncoordinated, housed in different databases or repositories.

• There is no single agreed-upon definition of resolution and centralized Knowledge Base.

• We too often rely on averages for CSAT KPI such as for many other metrics like handle time (AHT), instead of drilling down to the support staff level or the Interactions level. This is compounded by the lack of data that is granular enough to predict the performance of various aspects of the support staff.

The above challenges can be resolved by providing solutions for various aspects such as Personalized Experiences, Multi-Channel Communication, Interactive Content, Contextual Customer Feedback and Surveys, Community Building, Reward Programs, Social Media Engagement, Surprise and Delight, Proactive Customer Service, Artificial Intelligence (AI) Integration, etc., However, as part of this article, I will focus more focusing in the specifics as below,

A common definition of KPIs in each channel and across channels.

As noted there’s no consistent definition for Customer Engagement KPIs such as FCR, AHT, etc. for all types of interactions, and what is bandied around is called something different by channel. Businesses are to first use the same term for repeat, unresolved contacts, start where the customer starts, on their Omni channels, and dispatch with averages to get down to support staff and issue levels. Businesses can define such KPIs in several ways, the most popular being “the same customer did not contact us again within 7 days for the same issue”; however, the contact typically cited is in the Omni Channels/Therefore, Businesses should define their KPIs as “the same customer did not contact us again within 4 days for the same issue in any channel”, a tighter timeframe equating with customers’ greater levels of impatience.

Collection of data points at the Support staff and Interaction levels.

This step leverages the power of various technologies such as AI/ML, Big Data/ ETL etc., to mash up a myriad of data sources, and sort out the ones with the highest levels of predictive value. Here is where Businesses knock down “average” KPIS and overall KPIS across all issues, instead, Businesses need to collect data points across all channels, support staff, and issues, with issues are same as a limited set of reason codes in the customer’s language like “where is my refund?” or “how can I get another filter?” You will discover that the website’s KPIS is shockingly low (Businesses often see an average web support KPIS of around 30%!), meriting close attention to make web support dead simple.

By constructing an input-output table that shows which support staff resolve issues, and which ones do not, Businesses can finally get past the averages and roll up support staff into their teams to produce supervisor-based KPIS results. Here Businesses will see that some support staff’ KPIS is under 50%, pulling down the overall average KPIS, perhaps because of inadequate training, and some are almost perfect at 100%, meriting your attention to be sure to hold onto them.

Reactive ==> Proactive==> Predictive

Recommend not Business to directly jumping into the predictive models while they are adapting some new technologies, predictive models are only powerful when the current landscape is already Proactive but in most cases, they won’t, hence wise spending on any technologies or process only when the Businesses moves from reactive to Proactive and then to Predictive.

By having some of the hypotheses discovered, using technologies that can support the dynamic need of the business Businesses can find the cause and effect of probable drivers for such KPIs such as redesigned web support pages, new training, simplified knowledge sharing pages, and feedback to the Support staff (so that each one can see how their work affects customer satisfaction). Businesses can also begin understanding which customers and issues are likely to be Snowballs, enabling workforce management to route them to more specialized support staff with proven skills to melt Snowballs.

I hope this article has provided a few insights are how to improve on a few critical areas of customer engagement, will meet in the next article on “Efficient way of using or Investing in Predictive model”