To be in control of a customer service strategy, you must measure how successful your customer service is.

The good news is that most businesses do measure their customer service success: from Net Promoter Scores (NPS) to Customer Satisfaction (C-SAT); from First Call Resolution (FCR) to Customer Effort Scores (CES); as well as churn and full-blown Customer Experience (CX) surveys. Good news then, we must be in control of our respective customer service strategies. Let us give ourselves pat on the back all around.

But hold on. In many cases I see customer service strategy being used as an umbrella term, when in fact any measurement being done is only a review of a small subset of the whole customer experience. I have lost count of the number of times I have been asked to rate an agent or bot service with questions about timeliness, politeness and did they understand my question or concern, all of which are regularly 10/10. But were they able to solve my problem or answer my question? No, rating: 1/10.

It is a bit like my taxi driver asking me if his car is comfortable, smooth and the air is the right temperature, but taking me around the block in circles for an hour when I need to get to the airport. He needs to: 1. turn up on time, 2. in a suitable vehicle, 3. drive well and safely and, critically, 4. know where I want to go and how to get there. Measuring one part of the supply chain is somewhat pointless and damaging.

True customer service strategy is a big deal and complex. It must be integrated into the whole product or services that you provide. This could be a major career opportunity for you if you are dealing with fractional customer service. Owners of service strategy need to have a seat at the corporate strategy table and the data to back up any strategic changes that are needed.

Service strategy metrics differ greatly in pre-sales, service and support scenarios and, depending on your business sector, you are highly likely to need independent customer service strategy metrics for each scenario to track performance accurately and automatically. Again, all of these will be (should be) tied back to corporate business strategies and Key Performance Indicators (KPIs) or Objective and Key Results (OKRs).

Another critical indicator is how you capture and process data. I see so many engagements where that data is flawed at source. Critical service moments are moments that matter: escalations, churn moments and up-sell moments. We must get the raw data right throughout the supply chain. So many systems of record have out-of-date drop-down options, and IVRs and IVAs that are out of date. Additionally, agents who perform manual data entry may start the day with good intentions to use the right categories/topics/call reasons but, by the end of the day, the quality erodes. Furthermore, much data is not captured at all, for example surveys may capture only 20 per cent of the interactions (the top 10 per cent and the bottom 10 per cent) or capture in free text fields but not as part of any customer strategy data.

In conclusion, to gain control of your service strategy, you should follow these 10 best practices:

1. Measure the complete whole-product service
2. Measure 100 per cent of all customer interactions automatically
3. Link conversational intelligence to transactional records
4. Have regular interlocks with product management
5. Automatically detect and track unknown topic detection
6. Automatically detect broken processes
7. Automatically track Customer Friction Scores (CFS) across all channels
8. Measure service impact to revenue
9. Automate the C-SAT score without a survey
10. Monitor FCR based on conversational intelligence and AI

If you’d like to learn more, please get in touch.

 

 

AI-fuelled speech analytics has been around for the past 18 years. Initially, speech analytics was popular with enterprises, who were early adopters of this new technology.

The mid-market businesses followed, with more cost-effective speech analytics platforms that made their way into the stack in contact centres across the globe. Speech analytics today is mainstream, AI-fuelled and feature rich, with businesses seeking to further utilise and extend the use of their platforms to release the many business benefits.

Ultimately, the objective will always be to improve the financials, for example increase the top line, reduce costs and improve profitability.

Today, the business impact of AI-powered speech, chat and text, often called interaction analytics, should not be underestimated. Speech analytics can bring financial improvement if businesses make the most of what it offers. There are six questions you need to ask to ensure AI-powered speech analytics is benefiting your business.

1. Are you following your Standard Operating Procedure (SOP) and is this automated?
2. Are you finding new insights and opportunities that should amend your SOP and is this automated?
3. Are you detecting broken processes and is this automated?
4. Are you augmenting the workload of your staff and is this automated?
5. Can you truly automate customer interaction without damaging CX/C-SAT/revenue?
6. Are you mitigating and automating risk, compliance and maximising trust?

Are you following your SOP?

Every company or organisation has a defined SOP process for selling, serving, or supporting its customers or prospects. Some are heavily defined, some less so, but we all have them. One of the challenges is gaining a uniform standard of operations: the height of procedural excellence that covers knowledge, soft skills, process and a myriad of other dynamics. Speech analytics plays a critical role in this field as other data capture mechanisms are either gamed, incomplete, wrongly categorised or just not collected in the first place. Speech analytics and interaction analytics can give you an impartial view of the real voice of the customer and cover 100 per cent of all interactions. This delivers a phenomenal baseline and true visibility in a simple red, amber and green format of how the business functions across all channels whether they are answered by a human or a bot.

Are you finding new insights?

It is a fact of life that humans do not always get it right and the world changes at an alarming pace so, whatever your SOP, you should always be open to changing it. You should have the ability to automatically detect changes to your internal environment (for example product change) or external environment (competitor change) and be notified that this insight changes your SOP. You should know if you are no longer optimised, you are missing sales opportunities or incurring unnecessary costs. Advanced AI-powered speech analytics can automatically unearth unknown and new topics of conversation that do not fit into the drop-down field options and are not part of the defined knowledge set.

Are you detecting broken processes?

This can be lined up with identifying new insights but can also stem from systems failing or integrations and or knowledge not being in sync: the web says X but the IVR routes to Y. This is actually a great example. I recently asked a company what percentage of their calls were routed correctly. They said 93 per cent, which is high, but where did they get this understanding? They said that they extracted the switch CRD record and tracked all transferred call records. There were very few calls transferred. What they did not know was their agents were saying: “You have come through to the wrong department. I could transfer you but trust me it would be quicker if you redial and press 5.” The call was closed, with no transfer, and it was recorded as first call resolved (as far as the agent was concerned, they had solved the problem as the customer was talking to the wrong department).

Are you augmenting the workload?

The workload is not only increasing, but it is also getting more complex and more dependent on real-time solutions. Competitive challenges, pricing, packaging and super-complex delivery supply chains are dramatically impacting businesses. Additionally, we know automation is just scratching the surface in relieving the high-value, high-complexity issues and opportunities, and many of the simple interactions fail and end up with our human agents. Augmentation is a key asset in supporting the agent in high-pressure workload environments. Speech analytics assists in automating call classification, auto-summarising actions, customer intent, questions or issues, as well as prompting best next actions or ideas to assist the speedy resolution of a call.

Can you truly automate customer interaction?

Automation is clearly the future. It has such a direct impact on costs and, in many cases, convenience for the customer if it is done correctly. Today, we know that there is a massive failure rate, with frustration and damage being done in this area. This topic requires a blogpost all of its own as it is such a massive topic that spans into areas such as joining up the human-to-human analytics to capture failed automated experiences and updating knowledge, process or areas of service. Going further, we see a need to go beyond decision-tree automation – the press 1 for credit card, 2 for mortgage and so on – to a truly conversational experience, with the same level of understanding as a human agent.

Are you mitigating and automating risk, compliance and maximising trust?

This one does not need much explaining. It can cost organisations dearly, in money but also in reputation and trust. Add all these together and we arrive back at financial transformation. Closing the loop is essential.

If you would like to know more about Contexta360 and how we have helped businesses of all sizes navigate interaction analytics, please feel free to contact us.

 

 

Let us get straight to the point. The convergence of speech analytics technologies and the QM process is transforming the contact centre.

The benefits include a dramatic reduction in costs, time savings for both leadership and agent, an exponentially improved service to the customer, a shot in the arm for employee experience, and a new stream of business efficiency.

The QM function is absolutely critical. It may vary slightly from organisation to organisation, but the fundamental pillars are usually quite similar. We want to ensure a great service as well as security and compliance. We want to ensure our teams consistently follow a defined Standard Operating Procedure, that they have excellent communication skills, with strong product/service and process knowledge, aligned to the culture or brand of the organisation, as well as remaining compliant.

Phew! It is quite a job, but one that is critical as QM gives us the edge in a hyper-competitive, fickle and high-churn marketplace. If QM and speech analytics are used effectively, you will gain the following five capabilities:

1. The ability to automatically measure results not just actions
2. The ability to automatically focus your attention where it is needed
3. The ability to automatically discover shortfalls in what matters
4. The ability to automatically monitor ongoing performance
5. The ability to automate the sharing of best practice

But how do you unlock them?

1. Measure results and not just actions

A typical QM process identifies an area that may need improvement. This is logged and, where possible, a remediation process is initiated. This may range from asking for more attention to the matter, through to sharing learning modules or structured training. A review of the data may be set to investigate whether the agent believes they have improved: did they read the book, watch the YouTube video or attend the course? The real issue is the onus is on the team lead/QM manager to scan calls randomly for indicators of areas where improvement is needed. They may be lucky and find some. The agent may be lucky and the managers don’t find any. It is all incredibly unfocused and lacking in rigour.

The convergence of speech analytics and QM allows the QM leads to set metrics relating to the improvement areas. They can digitally monitor this by analysing 100 per cent of all the interactions across all channels and set red /amber/green thresholds to alert them to the right output.

2. Focus your attention where it is needed

You will have defined areas of interest. These may include first call resolution (FCR), average call handle time (AHT), customer experience (CX), customer satisfaction (C-SAT), product knowledge, soft skills and so on. Monitoring all the things that matter to the QM lead is a monumental task and impossible to do without a helping hand from technology. In addition, some of the datapoints that are available come from the system of record (i.e. what the agent entered) and may not be a true reflection of FCR or CX.

If you build a stack of metrics that are important to you, then use speech and chat analytics to scan 100 per cent of all calls and chat sessions, you gain an executive eye-in-the-sky dashboard that shows you where your attention should be focused. In most cases, we see 60 to70 per cent of staff do not need QM attention, yet they are regularly processed in the same way as those who do. Speech analytics and QM can enable you to allocate 80 per cent of your attention to the 20 per cent who most need it.

3. Discover shortfalls in what matters

As the QM metrics are now conversational queries, we are  able to set thresholds on all the metrics you need to monitor. Additionally, these monitor 100 per cent of all interactions, across all channels. QM leaders no longer need to spend hours looking for examples of deviation from the master plan, the system will find the proverbial needle in the haystack across millions of interactions instantly.

4. Monitor ongoing performance

If the system does find a potential area for improvement, this needs to be tracked and monitored. The convergence of QM into speech and chat analytics allows QM leaders to automatically set improvement tasks, review dates and attach snippets of the call that has been detected with that area of interest. You can not only track and monitor until a training note has been read or a video is watched, but you can automatically continue to monitor the performance of the QM metric.

5. Automate the sharing of best practice

Don’t just get the system to look for the bad examples. As you are analysing 100 per cent of all channels, it is easy to find brilliant examples of customer handling: from sales objection handling, to customer service empathy; and from advanced listening skills to great examples of call summary of actions taken. Then you can use the power of these platforms to share the examples or add them to your showcase library.

Next-gen QM; phew, what a relief.

 

Business trends have a habit of coming rapidly into and out of fashion in equal measure. Take the quantitative analytics to qualitative analytics trend, for example, or the great RS232 cable unplugging of limited contact centre ACHT, queue, hold, call transfer and disconnect metrics.

 

Today, the voice of the customer is still very much alive, but knowing why customers are calling, chatting and emailing, together with measuring response and ease of customer access, is highly challenging for many contact centre leaders. With customer expectations at an all-time high, having the right answers at the right point in time, with optimised processes and automated channels, is paramount.

While the challenges are numerous and the business is dynamic, change is the only constant.

Keeping people, processes, governance, product and competition in sync is critical, as is keeping knowledge and processes harmonised across all channels and service bearers, whether human, self-service or automated. Service delivery and competitive advantage depend on getting this right.

With today’s focus on NPS, CX, S-SAT, friction, effort and opportunity scores, we have a real-time feedback loop that ensures that all content, skill, knowledge and process is harmonised. But is it truly digital, or standardised or automated? Is it impartial, or subject to the emotions and points of views of an agent or customer?

Conversational analytics plays a transformational role in this field, allowing organisations to move beyond the highly dubious datasets that come from agent drop-down fields, free text fields, automation platform reports (that, of course, have a vested interest in telling you everything is working just fine…don’t worry) and emotional points of view. What Bob thought was different to Bill, Burt and Brian. Oh, and the Bot.

What is needed is an agnostic platform that can snap into an existing infrastructure, capture all channels of conversation, and transcribe, index and analyse with AI-fuelled conversational analytics. That is all the conversations, including topics, intent, sentiment, actions, questions, tone and many more.

Markers could be searchable and combined, and would enable and democratise an intuitive search engine. Think of it as Google that enables your entire teams’ call centre ops, central analytics, compliance, sales and product marketing. It allows your teams to ask questions of the platform and query millions of calls, chats and video sessions, and gain real and immediate insights into all the key metrics and trends from an impartial, unemotional and standardised format.

Contact us to find out how Contexta360 is helping contact centres rapidly deploy easy and open Conversational analytics.

Are you giving your pilots the information they need at the time they need it?

Air traffic control tells pilots in which direction to fly, for how long, at what speed and at what height. The interaction is quite dense on take-off and landing but can be quite dispersed during the long flight.

The pilot needs information to ensure they are complying with ATC directions, but also meeting the real-time smooth quality of service needed to keep 400 tonnes of aluminium in the sky with happy passengers every second. They need to make real-time decisions based on information in the cockpit.

If strategy, performance, direction and quality, business/call metrics or call analytics are our central air traffic (call) control, then what flight display are we giving to our pilots (the agents, advisors, supervisors and team leaders)? Are the metaphorical radar, distance measuring equipment, instrument landing system and so on all in the basement (AKA the analytics and IT team) having a weekly interlock to let you know how the flight is doing?

You may have crashed by then, or seriously damaged your CX.

Ultimately, airlines want to keep passengers safe and comfortable and delight them so that they come back. It is not too dissimilar in our world. We have to keep data safe, and we have to remain highly secure and compliant. We have to streamline customer journeys and deliver answers or service swiftly, and we need to do it in a way that they feel valued and respected so they want to come back for more and spread their brand experience.

Giving empowerment and true insight to the pilots and their direct supervisors is critical. Getting a weekly update on irrelevant data is not solving the problem.

I find that most pilots (agents) want to do better, want to know more, want to learn. They want more gold stripes on their arm and they want to delight. But do we have a good flight school? Are we measuring the right things, and is the access to this information available, reviewable and digestible, as well as easy, open and intuitive? And can they find this on the flight deck? In nearly all cases, I find the answer is no. The AT(C)C is old, closed, proprietary and very difficult to use, and only the elite have access. The pilots may have switch data, number of calls, average call handling time, call abandon rate, do not disturb time, wrap-up time and survey data (which I believe is flawed) but much of this was relevant when the Wright Brothers were flying. We live in a digital/hyper-speed world with next-gen jets. We need to empower our agents so they can see what matters: customer friction scores (relating to them), customer experience scores (relating to them), personal QM goals, security and compliance goals and real-time improvement plans. The business needs CX scores and C-SAT derived electronically via AI, not by the agent or customer who both have biased viewpoints and different scoring algorithms. The agent could be saying “I am flying at 30,000ft” while the customer is saying “I can read the number-plate of that car”.

The analogy is comparable with the old signs at the airport: click, click, click, LHR – JFK 001 09:20 GATE 34 CLOSING. That was about all you got!

Now, I have apps, flight schedules, I know what the plane is, where it is now, where it is coming from. I can change my seat at the last moment, add more bags and link data to my taxi driver. It is 100 times richer – and I am doing it. Easy and intuitive empowerment.

Let us give our pilots the apps they deserve.

Andrew White, CEO at Contexta360, examines the latest multiple-source data that highlights the monumental journey our industry has endured in 2021.

2021 has been a challenging sea of Cs for contact centres across the globe. Coronavirus consequences, change, complaints, control, confusion, compliance and closure, to name but a few.

While we must credit contact centres for keeping the wheels on the track during 2021, it has stretched operational and leadership teams to the limit. Contact centres have responded positively, providing consistency and confidence to customers during these very difficult times, but is it enough to save them?

The challenges ahead for 2022 and beyond due to COVID-19

Even if we could wish COVID-19, Alpha, Beta, Delta, Omicron, Omega or Zita variants away tomorrow, the wave of destruction and change surrounding us is significant and there are still huge challenges ahead.

On reviewing multiple data, depending on where you are in the world, the dramatic swing to home-based agent capacity has increased to 82 per cent and, in some unique sectors, even higher. There is now significant financial attention focused on whether physical locations are still required. You don’t need to be psychic to predict further office closures in 2022.

Call automation is increasing, reaching nearly 30 per cent of all interactions. But customer frustration is also on the rise. Contact centres are reactive and are deploying agents to pre-call handling to appease frustrations and to reduce call times. In a world of net promoter scores (NPS), the customer is truly king and contact centres are falling over themselves to retain customers at any cost. Customer experience (CX), customer satisfaction (C-SAT) and CES (customer effort score) barometers are running hot as leaders attempt to navigate change while also trying to reduce their call costs.

Call duration times are up a staggering 79 per cent. This indicates a wave of broken processes and missing knowledge due to the rapid redeployment of human capital, process and policy that have occurred throughout the coronavirus pandemic. Increases in call duration cannot be attributed to customers wishing to know more about buying or ordering products, or services and much more to do with failures in process or the customer journey. This is further exacerbated by average call time to answer increasing between 200 and 500 per cent and First Call Resolution (FCR) decreasing. The result is a perfect storm.

It is a massive conundrum

We have seen great change in the way we work and the way that customers interact and buy. In parallel, we have taken on significantly more compliance challenges. Remote agents have information outside of the physical and technical DMZ. Overall, I think we have done an amazing job, all things considered.

The consequences of the pandemic are far-reaching, from the customer service we deliver to agent welfare and experience management. Stress has two sides, internal and external, so we need to start changing how we do things and react and embrace these new market and service forces.

Automating and augmenting internal workload and the monitoring of customer journeys are now critical in my view. We are in a world where so much has changed and will continue to change. We clearly have a super-tight labour market, so looking after and developing our amazing staff is critical.

We also need to know what is working and what is not as this is directly linked to customer and staff stress. Automating CES, C-Journey, C-SAT, CX and QM are critical ingredients to understanding “why”. Why is the FCR so low? Why are call waiting times so high? Why is the call length doubling? How much of the automation is helping or hindering?

The list goes on but, in short, I predict the need for a new paradigm of metrics and measurement that goes way beyond the meta data we currently extract from our switches, clouds, IVRs and CRM. In short, clarity. The data proves it.

I am sure you have all heard the story of the frogs in hot water?

Just in case you have not, here it is. Take 10 frogs and put them in a bowl of hot water. They all jump out. It is hot! Take 10 frogs and put them in a bowl of cold water and start heating it up gently. They all die as they did not notice that the water was getting hotter.

The moral of this story is directly analogous to the customer interaction centre work we do day in day out. We get used to our world and our working practices – what we do and how we do it. We should challenge this and react constantly to internal and external factors to remain sharp, agile and competitive.

Question: does your bank call you in every week to see if the mortgage payment was made, the salary came in, and your balance is in the black? No, of course not. Are they are interested in this? Absolutely, but they have monitors, metrics, alerts and clever AI, RPA, CRM, thingummy-BOTs that let them know when thresholds are breached.

Bringing in several million bank consumers would be a waste of time as more than 90 per cent of us are probably liquid, in control and being financially prudent.

So why is it we still see 100 per cent of contact centre agents being hoarded into monthly (and OMG… sometimes weekly) QM or QA or coaching sessions?

Well because we have always done this. “Ribbit, ribbit. Go on kiss me.”

It is bonkers. Just do the maths. Let us say 500 agents and 30 team leads; once a fortnight for 30 minutes each; with management time and planning/research (hunting for the metaphorical bad mortgage payment that 90 per cent have paid) – that is circa a QUARTER OF A MILLION £$€ a year.

Or you can use this time to do WAY more productive and meaningful CX, C-SAT, CES work, transforming customer interaction and being a customer hero.

And that is just one example. There are so many more.

To help you to do the maths, we have built a simplified calculator online.

How we build services, technology and solutions has radically changed over just a very short period of time.

Much like the automotive industry in the late 1800s and early 1900s, the product was built from scratch. Nowadays, cars are built using a mix of industry components and off-the-shelf capabilities or micro-parts.

It is a similar journey for the raw coding world. In the beginning, we coded from scratch and some still do. Increasingly, we use abstraction software, drag-and-drop builders, and the auto equivalent of sub-components that do a specific task within the whole product.

This methodology directly impacts the customer interaction analytics world, specifically in the voice and chat analytics sector, and is having a marked impact on ease of deployment and cost of deployment.

In order to build a conversational analytics strategy, we first need to understand what it is. Conversational analytics is the convergence of speech analytics, chat analytics and wider text analytics across any medium (voice, video, app, mail or good old-fashioned letter). Ultimately, it is about:

• the voice of the customer
• what the customer is calling/texting/chatting about
• why the customer is calling/texting/chatting
• customer intent
• customer actions
• customer questions
• context of the conversation
• friction within the conversation
• emotion within the conversation

In short, it is the digital synthesis of a single indexed conversation, or trends and patterns across millions of indexed conversations.

Interaction analytics is very similar and typically focuses on the transactional and meta-data interactions.

Ultimately, it is about:

• when the customer called/entered a chat
• what the customer bought/inquired/requested
• where the customer clicked
• what options the customer chose
• the manual survey results
• journey paths
• security steps taken
• automation sessions (IVR, IVA, chatbot)
• channel hops made (for example chat to voice)
• IP/SIP packet data

When we blend conversational and interaction analytics, that is where the real magic starts.

But wait, we are still talking about tech. Let us move on to strategy as tech, which is the final part of the process.

What is it we are trying to achieve? The mission is not to be an expert in deploying conversational analytics, this is a tech capability that is needed to drive the original strategy.

So here I would like to assemble the framework of building the strategy. This includes:

1. The business results we strategically need
2. The business drivers
3. The influencing factors of the business drivers
4. The components of the influencing factors
5. How you resolve the influencing .

Phew!

But actually, it is quite a simple model, so let us turn the theory into practice.

1. I want to grow revenue by 20 per cent or I want to reduce costs by 10 per cent. (I’ll choose revenue)
2. The strategic drivers may be geo-expansion/product expansion increase customer acquisition/customer retention and upsell. (I’ll choose increase retention and upsell)
3. This could be new products, new pricing, new packaging, new promotion. But these are typically nailed down, so service would be a good driver. We need to ensure we have the right knowledge, service availability, soft skills, CX and C-SAT indicators to score our performance unemotionally and completely
4. In order to do this we need data from a) conversational analytics and b) interaction analytics. This includes what was bought, when it was bought, the key topics in all conversations, the intent, action, sentiment, emotion, question – and what insights we extract)
5. Finally, we get to the point of deciding how we get this data. In short, code from scratch, look at ready-made components (NLU and AI components off the shelf) or invest in a fully integrated solution.

Another dynamic to consider in your strategy is who in your business needs this insight? There is no fixed answer here, but increasingly we see two centres of value, namely:

1. The data-science teams
2. The operations teams that interact with customers.

Historically, everything mentioned above was the preserve of the data-science teams and the technology was highly customised, hand-integrated and manually “crunched”.

A few months ago I met a big brand potential client. It was interesting to see just how immature their capabilities were. Their process, put simply, was:

1. A business unit (BU) would request a report along the lines of “can I get the conversational analytics for product group X, in call centre team Y between Jan 1 and March 31 with indicators for CX, customer effort, and C-Sat (please)?
2. The data-science team would then:
   • Pull the recordings
   • Match this data to agent attendance data for the team in question
   • Match this data to agent attendance data for the product lines in question
   • Run the conversations through a speech-to-text tool
   • Run the transcriptions through their own natural language understanding application
   • Link this to transactional data and interaction data
   • Produce a report (in circa two weeks)
3. This would be received by the original BU owner who would say “thank you but this is not what I was looking for”
4. Repeat.

This is probably a bit extreme, but it highlights that the fact that our insights need to be way closer to the BU owners. There is still a very real place for the data-science teams to do more and more advanced analytics, but the example above can be executed in precisely 30 seconds in a modern conversational and interaction analytics solution. Additionally, data-science teams should not be too concerned about building from scratch. We have amazing supervised and unsupervised topic models off the shelf, including QM, C-SAT and customer effort score models and our own hyper-accurate speech engine that is tuned to your lexicons.

Contexta360 has all the components you need to build the speech and interaction analytics strategy you need. Why not get in touch if you would like to learn more.