Lots of things have changed in the world of sales, but some things have not. Building trust was important 50 years ago, and it's just as important today. Trust is critical for sales success. But today's buyers are busier than ever and, at the same time, have access to more information and choices. This makes their time harder to get, and their trust harder to build.
Selling data products is no different. You need to create trust between you and the buyer, the buyer needs to trust you and the products as well as the means to deliver value. But how? This episode of Data Economy Updates contains 5 aspects to consider.
You need to create trust between you and the buyer, the buyer needs to trust you and the products as well as the means to deliver value.
Tackle the top 5 qestions first
The presented aspects are by no means all-inclusive, but offers a good start for you. It is also important to understand that you can not control all the aspects of the data value chain, but there are things you need to accept as a risk. Tackle presented questions and you are doomed to boost your data product sales:
What am I buying?
What is the quality?
Am I paying too much?
Can I trust the source?
Can I trust the digital supply chain?
Let’s discuss each aspect separately and clearly state the problem followed by remedy suggestions.
Watch the video below or continue reading
First Problem: What am I buying?
In the article “Data Economy: Radical transformation or dystopia?” Scelta et al state that participants in the data economy can face chronic trust deficits and insecurity. People cannot often trust data. They do not know what they actually pay and what they get in return. In short, the buyer is a making a bet a bit like in a lottery. In the lottery, you often tend not to win. As a data product provider, you should not force your, customers, to guess what they are buying.
Remedy - is to provide a sample and technical schema. Another remedy is to use a Freemium plan, which has been found successful in the API economy. It offers me as a consumer a chance to see and try in practice if the product fits my purposes. In addition freemium offers a flexible method to learn how to use your data product.
Second Problem: Quality issues
Poor-quality data is often pegged as the source of operational snafus, inaccurate analytics, and ill-conceived business strategies. IBM calculated that the annual cost of data quality issues in the U.S. amounted to 3.1 trillion US dollars in 2016. Article in MIT Sloan Management Review 2017 states that correcting data errors and dealing with the business problems caused by bad data costs companies 15% to 25% of their annual revenue on average.
As a data product consumer, I need to trust the quality. If it’s excellent, tell me that. If it’s bad, tell me that as well. Not providing any quality values I am in the dark.
The remedy is to deliver what you promise: Define KPI for quality and use analytics, enforce quality in the data payload creation process, You need to define quality SLA for data products. Be proud of your product and keep the quality high.
Another Remedy is to handle expectation management. Clearly state in the data product the aimed quality level and be honest with it. It is better for the customer to get average quality data product content if they expect nothing less. Do not say you deliver 100% accuracy with 99,9% uptime if you can’t deliver it. Knowledge of the quality - and weaknesses - helps the customer to adjust their own processes accordingly.
Third Problem: am I paying too much?
People do not know whether they are paying more or less than others. What is even the price? Is this different for me and my competitor?
Remedy is simple. The industry needs a clear standardized data product model that enables comparison in various aspects such as price, update frequency, accuracy to mention a few. Customers need to be able to compare products and comparison requires standardization. Do not hide your data product price! Having pricing visible in the products instead of traditional "ask for quotation" note, consumers can make their price comparison more easily.
Fourth Problem: Can I trust the source?
Knowing where is this data really coming from is too often hidden. You just need to trust the claims. Consumers can not know if the organisation really even exists - perhaps this is a scam and this data has nothing to do with the real source. We are talking about trust towards provider as organiation, are they who they claim to be and is the data really about what it claims to be.
The remedy is to use a data platform as a mediator and trust-building bridge. Data Platform verifies the organization and people operating under their logo. That is what for example Platform of Trust does. What about the mistrust towards the data value chain? Using transparency can tackle the second trust issue. Use the data platform to show the verified and trusted data value chain from the source system to your desktop. Each organization and system involved in the data value chain is verified and each message is signed.
Fifth Problem: Can I trust the data supply chain?
Given that customers can trust the data source and the provider as was discussed above, then is the question of trust related to the data product pipeline. That is now expected to be built upon APIs, which have become the de facto components of any modern architecture. The consumer is thinking what is the reliability of the APIs? Are those really battle-tested and who is maintaining those? In short, can I trust the APIs?
The remedy is to use productized APIs, which have clear versioning and life-cycle. Another remedy is again transparency: customers can find live status information for the APIs, what is the response time, availability, and frequency of errors. The easiest solution for data product owners is to use a data platform to offer and maintain APIs for data product management and delivery. The platform of Trust has API -first approach in the development and thus APIs are treated as first-class citizens. That assures high quality and usability. You as the data owner need to take care of the value proposition behind the APIs - the data product.