According to IBM, businesses globally spend over $1.3 trillion each year in order to handle approximately 265 billion customer service calls. Self-service solutions, such as chatbots and knowledge management, can help businesses save on customer service costs by automating the handling of those customer queries, especially the most redundant ones. As usual when adopting and implementing a new technology, you’ll want to assess the return on investment your business will benefit from.
Customer self-service ROI
When monitoring the performance of a self-service initiative, there are a variety of different KPIs to keep track of in order to allow ROI to be measured.
One of the main indicators that can be taken into account is contact economy, which is based on the number of contacts avoided by phone or email. While figures vary depending on solution providers, it is commonly admitted that implementing a self-service tool allows businesses to reduce the number of incoming contacts (whether it is emails or phone calls) by approximately 30 to 40%.
The cost of the solution can then be put into perspective and compared with the cost of each type of contact when handled by a live agent. And even though these costs will be different for each company or industry, the price of a self-service solution will only be a fraction of the savings your business will make.
Determining the return on investment of self-service in general is a tricky exercise and it can often be easier to do the maths on a specific tool such as conversational chatbots or Knowledge Management.
How to calculate an AI chatbot’s ROI
Researchers predict that by 2025, chatbots will accomplish more than 90% of the B2C interactions. They also say that conversational chatbots can cut operational costs by more than $8 billion per year in the next three years. But if these figures are not convincing enough, let’s investigate what would be the return on investment of an AI chatbot for your specific company.
There are various elements to take into consideration when trying to work out the ROI of a conversational AI bot.
Identify eligible queries
First, you need to audit the queries your business currently receives over live chat and identify the ones that could be handled by an AI chatbot. These are the simple requests that can be easily automated vs complex queries, that should be directed towards a human agent. Note that the 80:20 rule usually applies, meaning that 80% of queries are simple enough to be handled by a conversational bot, while the other 20% will need human intervention.
Assess the average live-chat cost
Then, you need to examine the total amount of conversations handled per month; the number of live agents involved and the estimated time spent handling these queries. Multiply the hourly pay of your agents by the hours spent on these kinds of interactions and that gives you an idea of the average price per contact.
Don’t forget bot installation cost
Another element you need to factor in your calculations is the cost of setting up such a tool. This installation phase includes time spent on brainstorming sessions while planning the project, integration of the bot on your company’s website, as well as the training needed for the team that will be responsible for maintaining it.
This cost is a one-off charge that usually ranges from $3,000 to $10,000 depending on the complexity of your project.
Estimate monthly gains
Assuming that all the queries go through the bot and that it manages to solve 80% of them as stated before, then only the remaining 20% will require human intervention.
You can then calculate your monthly gains using the following formula:
Savings for chatbot-handled cases = 0.8 * number of monthly queries * average cost/query
Monthly maintenance cost
As for other projects, maintenance of the tool is necessary to ensure its performance increases over time until it reaches the level required and then keeps on operating successfully. Depending on the provider you choose, you will either get maintenance/support fees or not. You also need to dedicate some human resources on your end in order to ensure that the conversational bot is being optimized and maintained regularly.
As a consequence, the cost of monthly maintenance can be calculated as follows:
Monthly maintenance cost = provider’s monthly support fee + (time spent internally * employee(s) monthly salary)
Chatbots return on investment calculation
As a quick reminder, the return on investment (ROI) is a performance measure used to evaluate the efficiency of an investment. It is a ratio between the benefit (or return) of the investment and the cost of that same investment.
Therefore, the calculation will be slightly different for the first month, where you need to take into account the set-up cost, from the following months:
AI chatbot ROI for the first month:
ROI = (monthly gain – setup cost – monthly maintenance cost) / (installation cost + monthly maintenance cost)
AI chatbot ROI after the first month:
ROI = (monthly gains – monthly maintenance cost) / monthly maintenance cost
Calculating knowledge management ROI
According to a study by McKinsey, employees of a company that doesn’t use a Knowledge Management tool spend nearly 20% of their workweek looking for internal information or tracking down colleagues who can help with specific tasks. That is equivalent to one day per week per employee spent looking for information they need to do their job! I’m sure you don’t need to do the maths to realize that no Knowledge Management system is worth that much money.
But if you still need convincing, let’s try to work out the return on investment of a Knowledge Management solution.
First, you need to evaluate the costs of such a project. It is usually quite simple to calculate as it combines the cost of implementation and the cost of management.
The implementation cost is the sum of the software initial purchase, training and services required for the knowledge base to be placed into production for daily use. The cost of management relates to the ongoing costs of maintaining the knowledge base and the infrastructure that was implemented.
Estimate monthly gains
Actual savings made thanks to a Knowledge Management tool are more difficult to forecast than costs as they’re defined as soft dollars. Indeed, companies struggle to put a dollar figure on customer satisfaction, job satisfaction, quality of service, or even the intellectual capital that is captured into the knowledge base.
Let’s examine how some of the benefits can actually be converted to dollars.
1. Reducing the amount of calls thanks to KM
Forecasted improvement: 30% of current cases redirected to self-service
Savings = (Improvement * Cases per month) * Cost per case
2. Reducing the average call time on first contact
Forecasted improvement: 1 minute
Savings = (Cases per month * Cost per case) * (Improvement / Talk time for first contact)
3. Increasing the first call resolution rate
Forecasted improvement: 20%
Savings = (Cases per month * Improvement) * ((Escalated Talk Time – Talk Time for first contact) / Talk Time for first contact) * Cost per call
4. Reducing the escalation rate
Forecasted improvement: 5%
Savings = (Cases per month * Improvement) * (Cost per escalated case – Cost per case)
KM Return on Investment Calculation
As for the chatbot, the ROI will be different for the first month, as you have to take into account the set-up cost, than for the following months.The calculation is exactly the same as the one we did previously, but here it is again as a reminder.
KM ROI for the first month:
ROI = (monthly gain – setup cost – monthly management cost) / (installation cost + monthly management cost)
KM ROI after the first month:
ROI = (monthly gains – monthly management cost) / monthly management cost
Self-service additional benefits
Now that you have the tools to define the return on investment of your self-service project, you must keep in mind that some of the benefits of these types of solutions won’t directly impact your business in terms of savings or efficiencies, but will have some additional advantages that can’t be neglected.
All these quantitative KPIs need to be coupled with qualitative indicators such as satisfaction rate and NPS. This can take the form of a question such as “Was this answer helpful?” accompanying the response, providing a global vision of the level of quality of the tool. When performing well, a self-service solution will improve customer satisfaction, which will in turn, increase the chances of repeat business. Indeed, studies have shown that a happy customer will be more likely to buy from the same company again and will spend more than other clients.
“Customers who had the best past experiences spend 140% more compared to those who had the poorest past experience” – Source: Harvard Business Review
Another unknown benefit of a Knowledge Management system is the positive impact the tool can have on your SEO results. It can increase organic traffic by 10 to 15% and therefore help generate more business for your company.
Last and not least, a self-service solution will allow your business to enhance your customer’s knowledge thanks to all the data captured from your users’ queries. This rich qualitative data offers real actionable insights into your customers’ experience and issues – helping your company refine, change and develop new products or services as you see trends emerging.
If you would like to get more information on our self-service solutions and how these could benefit your business, you can schedule a call with one of our experts.