Flytxt, a consumer analytics and marketing automation platform provider for the telecom industry and broader enterprise market. Its platform, NEON-dX, has been deployed by more than 50 customers in 40 countries, serving over 600 million mobile consumers. Telecom customers include large operator groups such as Vodafone, Etisalat, Airtel and Zain.
In this interview Flytxt CEO Dr.Vinod Vasudevan talks about the status of data analytics deployments in the telecom sector, how Flytxt is helping operators to achieve top-line growth, and some of the enhanced capabilities that technologies such as artificial intelligence (AI) will deliver.
TM Forum: What is different about Flytxt’s approach compared to other solutions available in the market?
Flytxt: The key difference from a customer’s point of view is that we are focussed on delivering measurable economic impacts rather than just innovative technology solutions. A typical use case is a customer looking to generate an uplift in data revenues. Using our platform, they can automatically identify the right customers to up-sell and cross-sell specific data products. Then by comparing the change in data revenues from these customers against those who were not part of the promotion one can see the incremental impact of using our platform. Another use case is around churn. As a result of timely, intelligent retention efforts using our platform, targeted customers churn less than others. By doing such context-aware interventions, we have consistently demonstrated that usage of our platform leads to increased customer lifetime value.
We very much see ourselves as a top-line item. Clearly the opportunity for opex savings is there for our customers but we consider this to be secondary to the top-line impact generated.
TM Forum: Where are CSPs today in the deployment and use of analytics solutions?
I would say that the telecom operator community is only a third of the way to fully exploiting the potential of data analytics. We should not forget that analytics has been used in specific niche areas such as network planning for many years. But this tended to be just analytics on a desktop or a single server. When you look at how analytics is used across different departments, there has been very little progress. However, on the customer experience side, operators were one of the earliest industry players to adopt data analytics but they happened to use it in a fairly limited way.
There are two or three reasons that explain why it is taking some time to fully exploit the potential of data analytics. First, analytics has only recently started to get the attention of top management. You cannot do something that is truly transformational for the business without top management intervention given the traditional silos. But over the last couple of years, as big data has hyped up and CEOs have started to get asked about their analytics strategy in investor calls, it has gained more attention at a senior level.
Then there is the sheer complexity of the technology and the potential solutions. Some operators put in a Chief Digital Officer or a Chief Data Officer but then find it hard to work out how that fits with the existing business intelligence, product and marketing team(s).
I also think it is important to remember that data analytics is not a goal in itself, but a path to intelligent and ROI generating business decisions.
TM Forum: Which departments and divisions within telecoms operators do you work with?
Flytxt: We usually work with the same functions but sometimes they have different names. Typically, it is the CCO or the CMO, and the CIO on the technology side, although sometimes there is a new product director or a Chief Digital Officer. What we have seen over the last couple of years is some CMOs taking greater responsibility for decisions that might previously have involved the CIO and some CIOs taking responsibility for decisions that might look more like business decisions.
TM Forum: How much “hand-holding” do you need to do with your customers?
Flytxt: We want to make sure that we service our customers as best as we can, given that in many cases this technology is new to them. That said, we do see a spectrum. We offer a full SaaS model where we provide the technology with full support on a pay-as-you-go model. At the other end, we have the traditional model where we license our software and provide training and support, as needed. In between these two extremes, we see all sorts of combinations although we are certainly seeing the SaaS model growing.
TM Forum: Does most of your business come from operators who are in the early stages of deploying data analytics or from ones which already have a data platform and are just seeking to optimise their investments and strategies?
Flytxt: It is a combination of the two. We have many customers who had basically done very little with data analytics before we went in. But there are others who already had business platforms doing business intelligence work. We just came in and added more intelligence especially from new data sources or real-time capabilities. We are strong in all geographies apart from the U.S., China and the Far East. We serve both incumbent and challenger operators, in some cases in the same markets.
TM Forum: How do you expect your products and services to evolve over the next two to three years?
The NEON-dX platform has sophisticated analytics capabilities. We started many years ago using a certain level of intelligence to provide an assisted-marketing platform, automating lots of processes. We then moved on to provide users with augmented intelligence, like smart visualisation and exploratory analysis templates to go deeper in analysing data for taking decisions better and faster. The current phase involves the application of artificial intelligence in creating autonomous and fully-automated decisions. For example, the marketing application will decide which product is best for each and every customer at any point in time and consistent with their lifecycle stage, contextual need or even the business objective of operator. It is far better than what, for example, a marketer could provide given the complexity involved in analysing a multi-dimensional problem at such a scale. To put it in simple words, our products will increasingly take more and more intelligent decisions based on better and deeper customer insights.