Decisions to introduce a new product feature can open new doors to growth, differentiation, and customer satisfaction. Still, without adequate validation, they can also be costly and yield little to no benefit. When the idea hasn\u2019t proven to have real customer needs, engineering hours, design work, infrastructure costs, and marketing budgets can quickly add up, and so can operational resources. A validation-first approach can help businesses minimize this risk by testing assumptions prior to investing in development time and money. The teams do not need to take any proposed feature as an automatic enhancement; rather, they can involve the users in their research and development process to see how useful the idea is in solving a real problem through prototypes, experiments, behavioral data, and market feedback. This process might play a special role for a brand strategy firm since product decisions should be taken in the context of the positioning strategy and commercial goals of the brand. Validation is, therefore, a bridge between vision and investment with confidence, and it helps the organization separate vision and features that can deliver measurable benefits from those that are attractive.<\/p>\n
Product innovation strategy starts from identifying a clearly defined customer problem and not a feature that just looks techno-interesting. When launching into the development process, the teams should determine the pain point, familiarize themselves with the pain point users, and define the reasons why the current solutions are not enough.<\/p>\n
Interviews with customers, surveys, reviews, market research, support ticket analysis, and behavioral data can help determine if the problem is occurring often enough to warrant investment. Evidence should differentiate between customers who have simply expressed interest and customers who have a real problem that affects their buying or retention decision.<\/p>\n
A feature with a clearly understood problem to solve has a better foundation than an idea based mainly on internal assumptions.<\/p>\n
All proposed features include assumptions about customer demand, ease of use, pricing, technical viability, and business impact. It can be wasteful to test all assumptions at once, so it\u2019s important to determine the ones that pose the greatest risk so that you can focus your validation efforts on them.<\/p>\n
For instance, a team could assume that customers will use a feature a lot, are willing to pay for it, or will abandon an existing solution because of it. All assumptions can be expressed as measurable hypotheses that have a specific degree of success.<\/p>\n
By acknowledging uncertainty, you can allocate a limited research and experimentation budget to the most relevant questions to inform the development decision.<\/p>\n
It is not a rule that a fully developed product always needs to be validated. Prototypes, wireframes, clickable mock-ups, landing pages, demonstrations, concierge services, and limited-access beta programs can all be cost-effective sources of evidence without the cost of development.<\/p>\n
The selection of the testing method is dependent upon the feature and the assumption being tested. Usability can be measured in a prototype and interest in a landing page. A limited pilot can be used to determine if customers are interested and using an intended capability.<\/p>\n
The goal is not to make a small-scale copy of the product, but to provide valid information as to the merit of continued investment.<\/p>\n
While customer feedback is good, stated preferences are not necessarily a predictor of customers\u2019 behavior. The less specific the feedback, the less evidence you have that it is helpful to users.<\/p>\n
However, observed actions should be used as the basis for validation metrics as much as possible. The metrics that can prove useful vary by feature, but can be signup rates, activation, repeat use, conversion, retention, task completion, willingness to pay, or frequency of use.<\/p>\n
Behavioral evidence is more solid ground to make decisions on development, as it will show you what customers are actually doing, not what they think they are doing.<\/p>\n
The best validation occurs when there is a set of criteria established in advance for the transfer from experimentation to development. If decision gates are not clearly defined, teams can continue to invest in poor ideas for too long, because so much time and money is already spent.<\/p>\n
A decision framework can help categorize ideas as proceeding, refining, retesting, postponing, or stopping. The categories should be tied to metrics and business goals.<\/p>\n