PREVIOUS ARTICLENEXT ARTICLE
MISC
By 12 February 2018 | Categories: Misc

0

VIEWING PAGE 1 OF 1

By Wynand Smit, CEO of INOVO

There’s something fascinating about working with data – it’s everything and nothing, all at once. It’s meaningless without context, and yet context cannot begin to give it flesh. That’s where analysts come in handy, wrangling insights from the metrics that animate the information.

The brain is incredibly complex, neurotransmitters firing their explosive blasts more frantically than an emotionally-charged battle scene. Your body is too slow to perceive this, it just carries out voluntary and involuntary responses. Your mind is stranger still, an invisible web of connectivity and memory that’s holding it all together. And this is where the data analysts play, somewhere between the brain and the mind and the actions they produce – insights, potentially, too, that can allow you into a space where you can predict with relative ease what actions your customers may perform.

Data-driven insights

Insights come from knowing what you want from data; analytics must be governed by the appropriate metrics to yield useful information. What you get out, in other words, depends on how well you frame and define the research problem.

Let’s say you are interested in gaining insights from the data produced in your contact centre around agent activities and the service they provide. You can monitor the time spent on calls, successful conclusion of calls, call response time and so on; these are a good place to start, but they don’t necessarily give you enough information about the quality of the interaction: did the customer have to call several times? Did the agent rush through the call to boost call time metrics? Is the contact centre adequately equipped with sufficient staffing to respond to calls, and does the system accurately know when agents are at their desks? For the latter, if your system is routing calls to an empty desk while the agent is having coffee, for example, your customers are the ones suffering through long hold or connection times.

Beyond quantitative data, you can mine the conversations in your contact centre for specific negative words like “angry, unsatisfied, supervisor” etc. to determine trends and overall customer sentiment. This helps you to quantify qualitative data beyond predetermined and narrowly defined metrics to tell you if your customers are really happy with the service they’re receiving, or if they’re just getting the bare minimum.

More than monitoring your team, data-driven insights can also allow you to prepare sales campaigns that target the right customers, the ones most likely to purchase a specific product or service, or at a specific time of the month based on their profile data or behaviour. You can link those hot contacts with your agents most likely to sell using intelligent routing to improve conversion rates and sales performance.

Your data can tell you plenty about your customers, but it’s only useful to you if you then employ that knowledge. How many emails or SMSes do you receive that market products or services to you that are completely untargeted – car insurance to someone without a car, or education plans to someone who is childfree? Analysing data can help you to build customer intelligence that gives you the chance to market in a far more personalised way to the right people, showing them that you understand their needs and preferences and remain a relevant company and brand to them.

It’s all about developing a humanised version of the data you have, putting it in context for enhanced operations. If you get that right, a great Customer Experience is more achievable.

VIEWING PAGE 1 OF 1

USER COMMENTS

Read
Magazine Online
TechSmart.co.za is South Africa's leading magazine for tech product reviews, tech news, videos, tech specs and gadgets.
Start reading now >
Download latest issue

Have Your Say


What emerging technology holds the greatest potential?
Artificial Intelligence (12 votes)
Blockchain (5 votes)
Virtual Reality (2 votes)
High Performance Computing (3 votes)
Machine Learning (1 votes)
Nanotechnology (1 votes)
Computer vision (0 votes)
Edge computing (0 votes)
Autonomous vehicles (2 votes)