The data economy: why it matters for MVNOs

Topics covered:

1. Data as a currency.
2. Trends in businesses leveraging data.
3. What does this mean for Mobile Virtual Network Operators (MVNO’s).
4. Conclusion.

Introduction

It was Zuora (I think!) that first coined the term the subscription economy some 10 years ago. It strikes me though that the message of convenience (time and utility) that was the mantra of the subscription economy has been entirely overtaken by consumers feeling overwhelmed by these services.

Recently I read that the average number of subscriptions held by a connected adult was 16. These subscriptions cover content (e.g. OTT), services (e.g. grocery deliveries), memberships (e.g. Amazon Prime) and even cars. There’s a fair exchange between a consumer and a subscription business like these in terms of data, the consumer needs to supply some personal information (PII) for the subscription business to provide the service. As consumers we debate providing this PII to subscription businesses based on a fear of misuse, the company being hacked and so on.

Yet most adults make a choice to give data to many less legitimate, non subscription based brands that offer less of a legitimate service, willingly, more than 16 times a week (cough, OpenAI!)

What’s the old saying, ‘if you don’t pay for the product, you are the product’ – doesn’t that mean we’re squarely in the data economy now?

An economy that trades in the currency of data. Where data IS the currency traded – doesn’t that really mean we’re no longer in the subscription economy – but rather in the data economy?

Data as currency

Data has become currency. It’s not that different from cash in that this is an accepted and expected exchange for services to be provided – sometimes alongside cash but not always! We download apps, create accounts, provide data to access to our other data stores (e.g. identity), and therefore exchange our data for services.

And with ever more regulation and automation we provide more and more data. That’s especially true given the deprecation of cookies in an online context.

In fact, if you follow the McKinsey school of thought then you can see that the growth of spending in data is fuelling growth and out performance. Those that do end up accelerating their growth and efficiency hand in hand. Whereas those that don’t end up falling far behind and failing to catch up with the gains that are being created by early adopters.

Reference: McKinsey

An economy that values data

It’s not just that the fact that data is being exchanged, we know that society and our economy values data highly when efforts to regulate become a central theme and source of power.

Great examples of this are the legal frameworks that are now in place to protect data and identity e.g. GDPR and Privacy Law Reforms. Our personal data is now highly valued, and we personally take efforts to secure it.

With the widespread adoption of AI I believe this is just the beginning and we’ll see more and more value placed on data – both personal and generalised.

The data archetypes in business

I see four broad archetypes emerging in business’s use of data. These are:

1. Blind ignorance
2. Security & data as a service expectation
3. Data as differentiator
4. Data unlocking revenue streams

1. Blind ignorance

There are certainly brands that are still living with the mistaken belief that they do not need to operate with data. The brands that don’t survey, don’t have a data strategy, don’t really use BI and that don’t invest in harmonising data and letting it be leveraged in their business. 

The problem with this approach is that if you are in a competitive industry and your competitors are doing these things, scaling your business at the same rate as them will ultimately be a challenge.  Why? Because you don’t benefit from the compound business case of AI built of a solid data strategy – the operational efficiencies, the operational agility and the superior decision making.

I think this trend is nearing it’s natural conclusion – RIP!

2. Security as a service expectation

It seems almost a minimum standard to be ensuring that your brand protects your customers data. 

As telco’s this seems almost as important as talking about your customer service availability or having an app. It’s not unreasonable for your customers to want to know that the data that they share with you is protected. 

The trend I am seeing is the increasing legislation, standardisation and compliance that makes this substantive – not baseless. ISO 27001, Telecommunication regulators and efforts to police opt in and opt out are terrific examples of the proof points that are necessary for telcos to prove to themselves and their customers that data is being protected.

Even MVNO’s should count themselves as included even when the standards don’t seem to apply. In the data economy – you too should care about your customers data as much as they do. These aren’t just the standards that apply to the carriers or operators out there under the spotlight for non-compliance and the target of malicious hackers.

3. Data as a differentiator

In many telco markets data allowances are unlimited. Data and price aren’t really enough to differentiate – unless you are acquiring from the ‘value segment’ also known as the rotationally churning segment. You need to differentiate through your brand and proposition. But how do you know you appeal to the right customers, how do you know that you are acquiring the right customers (not just the value seekers), how do you scale when you hit upon the right combination of product, pricing, brand & target audience.

Well, you need data. You need to know your audience – deeply. If you were a toothpaste brand it might be ok to know your audience at an anonymised level but when you have a subscription relationship with a person and your product is somewhat ubiquitous, data is the only way to understand your customer and their actions, behaviour and motivations.

It’s easy to pass over the data-differentiators because they are usually also digital first brands – Netflix, Spotify, Uber etc.  But how do they create virality and exceptionally personalised experiences….data of course. It’s not just the fact that these brands are digital first; it’s that being digital they build a data foundation from day 1. Data is at their heart – it’s in their processes, their products, their DNA and that’s how they create difference.

Remember the robot vacuum cleaner called Rhoomba? To me it was the ultimate example of a data led strategy. Amazon made them an astounding offer to buy their Rhoomba – why? Because Rhoomba had mapped and stored the internal footprint of millions of homes – you can just imagine the usefulness of that information when you are considering what to sell or cross sell from an Amazon search or purchase.

For telcos this of course is a little harder because for the Operators out there they were traditional utility subscription businesses that now need to become data-differentiators. For MVNO’s it should be easier to be data-differentiators. This strategy is definitely trending….but beware the tendency to be data-differentiator on one hand and then revert to form as an opportunistic marketeer at the same time. Why – because if you’re confused about what your business really is then so is your data.

4. Data unlocking revenue streams

And now to those that manage to turn data into extra $$. This is the emerging trend to watch out for.

Data can be a revenue stream in its own right. Especially for telcos – who benefit from collecting and processing bucket loads of data – more than the toothpaste brand that we talked about earlier. You don’t need to be selling personal customer data but you can consider monetising aggregate data and also tied to the data-differentiator trend build yourself the ability to cross sell related or valued added services to your base.

Apple are the masters of this. In 2014, at a time when Apple was shipping perhaps 50 million wired headphone units (pre the ear bud revolution) a year they made the unlikely move to acquire Beats. Why? Well, they were gearing up to revolutionise the music industry with a music streaming platform – they didn’t really know their audience – they needed data. That’s why they spent $3.2 billion buying Beats – 10 years later that $3.2 billion that was pretty well spent. Consider that in 2023, Apple’s revenues for streaming were $9.2 billion in one single year!

So telcos and especially MVNO’s the question is …. what data do you have that you can monetise AND how can you use that data to build new adjacent revenue streams. Your competitors are already thinking about this, so how are you going to put the data foundations in place to enable this in your business.

What this means for MVNOs

Ok, so you don’t have the billions of dollars that Netflix or Apple have but nor do your Carrier (Operator) neighbours. There are some simple things to do to get you off the starter blocks.

1. Telco specific data platforms: If you haven’t invested in a data platform it’s time. There are plenty of off the shelf versions. A word of caution though – your telco needs are specific so make sure you’re choosing a data platform that fits your budget AND your use case.

2. Mature your data management capabilities: The tooling is one thing but for every piece of data you originate make sure you look after what’s being put in, what changes are made and what comes out. This is as much about people and process as it is about tooling.

3. Broaden the scope: It’s likely that you are already generating some great data – MVNE’s and BSS platforms are fantastic sources of great quality data – but what else. UDRs, CDRs, preferences, segment behaviours- think about what else you have and how you can leverage it, or better yet, monetise it.

4. Data services: Enrich your data with available data services to make it even more useful. There are a myriad of sources available – competitive data, home ownership, demographics etc. These turn useful data into highly targetable data sets.

5. Know the analytics needs of your users: All of them! Know what they need, how they intend to use it and treat them as the customer of your data. BI is so 2000’s you need Predictive Analytics – What will a customer do next? What do they need now? What will they respond to?

6. The business case: One of the big gaps in the adoption of a data strategy is a business case. It’s the reason that so many telcos started with a bold strategy and ended with a churn model to predict churn. It’s the greatest tragedy in applied machine learning and AI to see everyone race to solve the effect not the cause. As an example, the business case for churn is not just in saving customers when they are all but gone, it’s going upstream to acquire the best customers that will stay and will advocate for you. Understanding the business case then measuring your outcomes is how you make sure that you’re creating ROI not just ‘wow cool’ from your data investments.

In conclusion

MVNO’s, like it or not you are operating in the data economy. You’ve got to go beyond being a tech business to becoming a data business. If you do this from the beginning it’s easier than going through transformation.

I hope you’ve got some useful ideas about the data foundations you need to win in the modern data economy – at Sourse we help telcos build their data strategy and adapt to their AI driven predictive future. Please be in touch if you’d like some advice.

What’s next in this Series: The business case for data and AI

I’ll be writing a follow up article in December building on this blog. In the meantime feel free to be in touch if there’s anything specific that you’d like to know about the business case for data and AI for MVNOs.