Interaction Analytics: The Core Benefits

Interaction Analytics is becoming well known in Customer Service circles.

Particularly in its single channel version many already recognise as “Speech Analytics”.

As a result, there is a certain buzz about its potential. Analysts forecast double digit growth over the next few years. Let’s hope so.

Moreover, the extreme excitement already whipped up around Big Data only adds to the expectation around what Interaction Analytics can do.

In terms of that bigger picture, Interaction Analytics is just one of the jigsaw pieces currently coming into view.  Over time it will fit into a broader analytical framework for real time enterprise decision making. This will happen as overall understanding of Big Data best practice matures and other forms of customer analytics such as social and online converge.

Previously, I provided an overview of why customer service leaders should be insisting on this capability. The purpose of the post you are now reading is to summarise the range of benefits that Interaction Analytics delivers today.

A well structured program should be delivering a range of both quantitative and qualitative benefits.  I’ve provided three areas of benefit you should consider for your Interaction Analytics business case.

They are not listed in any particular order. Their respective value depends on your own business priorities. Quite apart from the bandwidth considerations of tackling everything at once!

#1 Reduced Opex (operating costs)

Customer service costs are a function of the volume of customer interactions. Therefore removing them, shortening them and completing them first time has the effect of reducing the associated headcount and infrastructure costs.

A focus on improving NTT (non talk time), AHT (average handle time) and FCR (first call resolution) are often early wins when an Interaction Analytics solution is first deployed.  This is where the low hanging fruit is normally found.

Equally, investigating why customers need to interact with you often shines a light on flawed customer journeys. Both within the contact center and cross silo. This is typically another huge bucket of opportunity.

But where do you start?

Customer Journeys

Typically by looking at how customer interactions are classified based on initial call drivers. This helps you focus on relevant topics from which you can then develop a hypothesis of what is happening. Think Sherlock Holmes sleuthing!

The next stage is confirming you have the right idea. Additional reporting and drill-down capabilities gets you to root causes. If all goes to plan, the ‘what’ and the ‘why’ are then established.

The workflow then moves on to establishing how often an issue has occured. Your goal is to quantify the value in resolving it. Once known, some organisations will discard isolated incidents in favour of ‘big ticket’ opportunities. Others take the view that each customer is worth reaching out to. Especially given their ability to escalate via social channels.  It is your call.

Whenever it is decided to green light an issue, the last item on your checklist is the improvement plan. Sometimes the need to make contact can be removed altogether. Other times the steps in a service request can be simplified, reducing time taken to reach resolution. Either way, it’s an occasion to call in your best UX designers.

Notice how Interaction Analytics has been used to drive evidence based outcomes. In this scenario the focus was to quantify the impact of customer journeys that didn’t work. This prioritised the ones that mattered to most customers. The outcome provides you with the first tranche of high yield opportunities to drive costs down with quantified benefits.

Experts report these types of initiative contribute 1-3% savings in opex budgets within the first six months. That means the investment in Interaction Analytics often pays for itself half way through your first year’s program!

Advisor Effectiveness

Of course Interaction Analytics listens to both the customer and advisor side of the conversation. That means you can also expect cost reduction benefit in your performance, quality and compliance management programs.

Some of these might take longer to realize. Such as a reduction in attrition through improved coaching. This is given some of the cultural and competency changes probably needed. Nonetheless, there are huge benefits to be harvested here. Some around opex, some around customer experience from improved advisor engagement.

Here is the opex orientated list.

1. The role that Interaction Analytics plays in automating aspects of quality monitoring is an instant win if you need to reduce that headcount
2. The financial threat of being fined for non compliance is a widespread concern. Interaction Analytics transforms  the process and effectiveness of managing this risk
3. Attrition and absenteeism costs should fall as your improved performance management and coaching interventions impact morale and job satisfaction

In summary, Interaction Analytics will help across the entire improvement cycle of finding, prioritising and ‘managing to completion’ opex reduction opportunities.

#2 Improved Decision Making

Contact centres have been reliant on the reporting available from traditional infrastructure. The result is an unbalanced basket of KPIs which are primarily internally focussed. They are also often divorced from broader organisational objectives.

Thus decision making is undertaken without sufficient awareness of what is really going on with the major stakeholders in Customer Service: customers and how their behaviours impact business performance owners.

Interaction Analytics changes this by being able to answer these types of question.

1. What is the scope of the problem or opportunity we need to make a decision about?
2. Once an issue is recognised, what trends are important to keep tracking?
3. What is impacting customer behaviour in relation to our intended business outcomes?

In effect, Interaction Analytics becomes a key tool for operational problem solving and an ongoing source of management information.

In fact by the end of year one, you should expect to see managers and team leaders actively using dashboards for their everyday decision making. Strategically, you should also expect key recommendations that have emerged over the year to influence year two budgeting and planning.

As your three year program progresses, other aspects of decision making will continue to make progress.

1. Your responsiveness to changes in customer behaviour improves based on embedded alerts and in depth insight. You notice more and react faster. Your ‘Problem to Resolution Cycle Time’ looks much healthier
2. Your accuracy in annual forecasting improves because you know more about the variables and have therefore improved your ability to control headcount demand
3. You are able to run year on year benchmarking on any metric or trend that needs watching. Ongoing vigilance reduces surprises
4. Cross functional decision making gets easier as confidence in the analytics’ output matures. Using meta data to enrich insight and make it more useful to other functions’ decision making needs becomes another milestone along the way
5. Your insight based relationship with Marketing should be mutually active and fruitful

In summary, you improve your ability to prioritise issues that matter to both customers and the business.

#3 Positive Customer Behaviours

Most agree that Customer Service is a key part of an organisation’s overall effort to nurture a healthy customer base.  But, quantifying the level of contribution and finding an effective way to track it, remains a common issue.  Interaction Analytics builds the links between organisational and customer behaviours. Then to the commercial outcomes coming from that interplay.

The first milestone in this link building is to establish what impacts customer satisfaction. Using a combination of ‘deep dive’ analysis and subsequent trend tracking, improvements can be quantified as and when broken processes are fixed and customer journeys get redesigned.  Incidentally, the same impact can be tracked for non service triggers such as price changes, new customer friendly policies etc.

Whether you use NPS (net promoter score), Customer Effort or Customer Sentiment (if tracking customers over social channels), Interaction Analytics helps you establish baselines and subsequent trends. This is a powerful addition to any VoC (voice of the customer) initiative. Owners of such programs should be pleasantly surprised about the relevance Interaction Analytics has for them.

What else?

Beyond this initial point of tracking how customers rate their relationship with you, customer retention becomes the next ‘proof point’.  It validates whether what they say about you actually translates into loyalty.

Interaction Analytics programs in their second year are typically able to identify customer attrition trends and what has caused them. This insight can be then fed into broader analysis. Such as comparing ‘cost to serve’ figures against average customer acquisition costs. This helps determine where budgets are optimally used. In other words prove if it is smarter to save a customer or find a new one.

Once the drivers of customer retention are understood, more ambitious analysis can be undertaken.  For instance, customer purchasing behaviours can be overlaid as meta data to unearth any correlation between certain organisational behaviours and the propensity to purchase.  Once identified, these can be codified into KPIs and alerts, allowing the service organisation to maintain focus on profitable activity.

The same applies to customer churn. I’m aware of some really exciting work currently being undertaken on this. The accuracy of forecasting customers’ propensity to churn is being dramatically improved. This happens when data models built from transactional and demographic sources are enriched with interaction data. This is true big data mashup. Right now, there are a few big brands getting very interested about how this impacts the effectiveness of their outbound retention campaigns. Leading edge stuff.

In summary there is a wealth of behavioural clues sitting within your daily interaction stream that will help you recognise and encourage ‘positive’ customer behaviours: brand advocacy, duration of brand loyalty, increased average and lifetime value.

In Conclusion

When undertaken with commitment, skill and focussed application, Interaction Analytics delivers a transformational level of benefit for traditional customers service operations.

This is timely.

As digital customer service takes holds with the offer of real time personalisation, customer service leaders cannot afford to be gazing into their rear mirrors. Interaction Analytics is the toolkit that gives them a fighting chance.