Technology is amazing, for many it’s an equalizer, bringing the world to our doorstep - regardless of where our doorstep is; all the good, the knowledge, the services. It’s also brought all the bad - the unit economics of fraud have been optimized to the extent it touches most income groups, moral dilemma discussions abound around data selling and AI bias, and at the more benign end billions dollar markets have emerged to simply “make technology work”.
Too often product market fit is overlooked in terms of product revenue fit. Making sure the product meets the whole needs of the market - that it can integrate within differing environments, works across the needs of all the functions it touches, has no messy unintended consequences (either financial or moral) etc. can be expensive for companies. Proving product revenue fit is much cheaper and can be resolved with a simple sales bookings number – no questions asked!
Product revenue fit is certainly a short-term leading indicator of product market fit, and shouldn’t be ignored, but neither should it be the only goal for long term business success through customer and market satisfaction.
What is the key to achieving product market fit? It is looking at both the capability of what is being developed to address unmet or underserved needs, as well as how it can be applied within the market. This applicability needs to look from a practical, and a moral compass.
Understanding how technology will be used, in which environment is key. At Callsign the backbone of our development team don’t come from software vendor backgrounds, but instead from banking, ecommerce, telecom organizations. At first glance this might not seem important, but when you put it through the lens of product market vs. product revenue fit, the criticality comes to life. Our engineers spent their former lives putting band-aids on technology solutions to make them work in the real world - to integrate with existing infrastructure, and to work well across all functions, not just the principle decision making one. They designed to encourage functions to work together across customer experience, fraud, security and risk/compliance; they also designed for ease of use, having experienced the “pain of use” systems designed by those who have never done the job.
As a result, when we are developing at Callsign just as much time, care and attention is invested on how our software can practically work for our customers, as is spent ensuring that we continue to innovate at the bleeding edge of identity.
Another example of this is the modularity of our platform, it has been designed to fill in the gaps of customers’ identity challenges. Understanding that different customers will have already made investments, either through buying from traditional technology companies or building internally, our platform elements can be purchased individually depending on the challenges our customers are looking to address. So, whether the focus is on operational elements such as reducing call center or fraud management costs, or more growth focused such as increasing transaction completion rates or increasing positive review and NPS scores, Callsign works with you to achieve your specific goals. This means we remove the overhead of erroneous and unwanted features built into the price, and your solution is designed to work with your environment, not the environment the traditional software developer thought you might have - in her/his ideal world.
Here it is about understanding the implications of how the technology could be used. Our whole raison d’etre at Callsign is to make digital identity simple and secure. Because we are in the identity sphere, we are highly conscious of our responsibility to protect not just our customers, but their clients too. Be it a multinational bank and their customers, or a retailer with an online business, our job is to allow them to confirm the identity of their clients without doubt and without extra hurdles that might make their clients abandon what they were doing or go elsewhere.
In the identity space the big question tends to be around data privacy, just what data is being collected to confirm a user is who they say they are. The first thing to state is that Callsign designed our software so that we don’t know who any user actually is, we only know that they are user xyz, of customer abc. But that isn’t enough for our customers and comes back to the applicability vs. capability point. Our customers need to make a commitment to their clients that they aren’t collecting extra, unnecessary data and they need to offer clients a choice of ways to be identified (not everyone wants or has the ability to provide a fingerprint via a smart phone).
The applicability of our approach comes to the fore:
Minimal data collection
Traditional fraud solutions whose purpose it is to identify that the user is not who they purport to be, use all their capability to do this. This can mean monitoring the web browsing habits of the user, or a concept called continuous authentication which is often referred to as appropriate surveillance (appropriate to the software company that is, not the customer, or their clients). Because Callsign applied our moral applicability compass to our approach, we only collect data at the time of the interaction, and only data that is used within that interaction. As a result our customers can pass thought the most stringent privacy lens.
Pragmatic data collection
The “best” identity journey is often described in the terms of the latest technology – biometrics, chip implants etc. without a thought for whom this journey is applicable for. So many questions arise, do clients want to share this data with their suppliers, even if they have the means to do it? Irrefutably if our biometric data is compromised, we cannot call a helpdesk and reset it, and that causes concerns for many clients with some suppliers. To make a solution work for all Callsign designed a dynamic decisioning manager that can adjust in real time to a client’s preference and capability for data collection.
In the AI space, there is much discussion around bias within the models many challenges have occurred with solutions once their capability has been proven in lab conditions, and real-world deployment has thrown some applicability challenges to population sectors. It is important to learn from challenges and collaborate across industries, governments, influencers and geographies. That is why we are grateful to the World Economic Forum for inducting us into their Technology Pioneer cohort, it means we can collaborate on best practice frameworks such as the Framework for Developing a National Artificial Intelligence Strategy. We are delighted that they have chosen us to be a best practice case study in the second version which will be released in early 2020.
Of course, the capability of the technology is always what will attract interest from potential clients and investors in the first place, what need is looking to be addressed. But, without looking at the applicability of the solution too, customers are setting themselves up for needless and unexpected expense in a practical sense, and reputation threatening unintended consequences in a moral sense.
When choosing a vendor to partner with consider the whole product market fit, and make sure you ask the questions to allow you to determine if you “should” partner with this vendor e.g.:
- Do they hold the appropriate ISO certifications?
- What is their AI governance?
- How do they integrate within our environment system?
- How easy is the solution to manage?
- Does work for all the teams that need to use it and encourage collaboration?
- What extra data are they collecting, does it match company standards?
- Is there a reputational match from a brand value perspective?
It is important to recognize that this approach does come at a cost, again using Callsign as an example we invest a disproportionately high amount in technology and, because at the end of the day, you cannot sacrifice investor return and stay in business, we need to manage overall spend carefully. But we believe that our customers will be happier and product market fit beats product revenue every time.