5 Reasons Why your Chatbot ROI is Stuck
As automated solutions such as chatbots, conversational IVR and intelligent voice routing become the norm for businesses supporting customers and employees, pressure is increasing for project managers to prove and maintain the return of investment on these technologies. And, considering the potential of a global recession arriving this year, then cost and resource spending becomes even more crucial within businesses.
Based on +20 years of experience developing conversational solutions for some of the largest organizations in the world, we have identified 5 possible reasons why your project’s ROI is stuck and why that is making it more difficult for your team to get further internal support and resources to maintain project growth.
Reason #1 – Focusing only on the short-term
There is nothing wrong with starting small, but we see that the most successful implementations draw a long-term strategy with a vision to integrate Conversational AI across different business units and use cases. To achieve this, the team driving the project needs to understand the benefits and possibilities of what can be achieved with the technology. It is a hard task, but this is the most important decision you will take as it will impact your ROI, resources, and strategy for years to come.
Remember to put the focus on the business challenges your organization is trying to resolve instead of focusing just on the technology; it is not that technology is not important but having a clear idea of the problem you are trying to solve and what needs to be achieved in the shape of hard metrics, KPI’s and data. This will help you identify the best fit for your existing technical architecture, which takes us to our 2nd point.
Reason #2 – Picking the wrong tool for development
Conversational AI vendors have a tendency to indicate that they have the ‘perfect’ tool for your team and talk about about how easy it is to develop Conversational AI solutions…. So, let us get it out of the way… Conversational AI is HARD, and it gets even harder when developers try to over-humanize them. No system is as powerful as a human brain, and customers do not want solutions passing themselves off as actual humans.
Customers are comfortable using self-customer service to save time, but that does not mean that all processes should be automated; there are some cases where human agents will always do better, and customers appreciate the shift from self-service when the system cannot help.
So, whether you are exploring CAI for the first time, or struggling with your own ‘stuck’ implementation, we suggest that you consider an enterprise CAI platform that:
- Allows for scalability: select a platform that will grow with your project and lets you do more with less. Whether it is the implementation of different languages, multiple use cases or connecting across channels, make sure that you pick a tool that will allow you to deliver on your vision (see reason #1) and scale your solutions in an efficient way.
- Is proven beyond purely technical functionality: it´s good to do a check for the functionality you need and to compare among vendors, but as you start researching, you will notice that pure features are standardized, and it gets hard to differentiate why you should pick one from the other. Look beyond features, for those platforms where successful projects have been built and are closer in line to what you want to achieve in your long-term vision rather than just what is coming on the product roadmap.
- Works for different roles within your organization: building successful conversational solutions needs a multidisciplinary team that mixes business and technical profiles. Having a platform that can combine low, no, and pro-code will ensure that everyone in the team will be able to contribute and work together without killing the potential of very complex technical integrations. Simultaneously you need a platform that enables and enriches the collaboration across these multidisciplinary teams. This is a huge factor in creating solutions that efficient scale.
- Plays well with your existing infrastructure: there’s no point getting something new if you need to replace everything you have already built (and paid for). Hence, look for tools that can optimally integrate without making it a burden for your technical team to start all over again. No tool will magically work out-of-the-box with your pre-existing architecture (no matter how many new connectors they claim to have release every week). Pick one that has proven integrations in projects that have similar circumstances than yours. At the end of the day, you will only be using those connectors that are relevant for your own technical infrastructure, so the number of connectors available a platform has should not really matter unless they are right for your company.
Reason #3 – Selecting the wrong use case
In the spirit of being conservative and starting small, we have seen companies fail with implementations due to picking the wrong use case. Although it may seem safer for a company, limited solutions tend to lack visibility for end users and end up with very low traffic. Even if clear KPIs have been set from the beginning, the shortage of data means that the company cannot determine if the project should be scaled and whether the goals have been met.
The first question, is to determine whether you are picking the wrong use case… does it really need to be automated? Not every process needs to be, so make sure that you work with a team experienced on implementing Conversational AI solutions that can guide you to the areas of the business that can serve as first projects (look for the low hanging fruit), without taking your mind off the long-term vision.
If you plan to start small because you want to learn, focus on getting that learning outcome and not expecting the use case to have dramatic ROI or to prove your longer-term use case potential. Take it for what it can be (a learning exercise) and then move forward with the next use cases that you have planned for.
Furthermore, other actions that may seem logical to protect the exposure of the new project may end up hurting the chances of it being able to scale and grow; from hiding chatbots in remote landing pages, limiting the knowledge of the solution to very few topics, keeping it small for a long time to not promoting the solution at all, there are many ways to kill a project without giving it the opportunity to thrive and show its potential.
Reason #4 – Keeping your chatbot in a silo
Data, data, data. By now, most companies recognize the value of mining the data they are getting from their customer interactions to improve processes and take better business decisions. Yet, we still see a lot of companies failing to share data between departments, keeping their respective projects siloed.
With the increase of voice interactions and customer conversations carried on through multiple channels, Conversational AI provides an invaluable tool to centralize all this data to provide insight into what customers are truly thinking and needing. This does not need to be limited to the data pulled from implemented Conversational solutions but can be extended to potentially any system that customers are using to interact with the company. If your company can start predicting and anticipating your customer’s needs by connecting the dots across systems, customer experience will improve.
Furthermore, your whole Conversational AI strategy will have a stronger potential for scale and efficiency by having a centralized and shared platform where resources can be used across different business units, and support can consolidate.
So, start sharing and let your solutions connect to as many of your existing systems as possible to take advantage of data you already own.
Reason #5 – Ignoring your customers
So, let’s say you are not guilty of #4 and you have all your system connected to your conversational solution (well done!) but… are you adjusting your strategy, services and products using the direct feedback that you are getting from your clients? Conversational AI is all about the customer; we try to make their lives easier, save them time and create better experiences for them. Hence, data from these interactions should be used to ensure that these benefits continue to increase over time.
Listening beyond the Call Center, identifying trends and analyzing data from different sides of the business will help your team have a better understanding of the Customer Journey and their experience. With this information, you can drive better sales, marketing, and product development strategies.
At the end of the day, there is no single silver bullet to make Conversational AI work for your business. It is a contribution of factors and decisions but if we can summarize this article into one, it would be to embrace the technology as part of your long-term strategy. The only way to keep ROI of a Conversation ai solution increasing is ensuring it expands across the organization finding new cases and business units to use it and ways to continue to scale the ongoing projects.