Predictive Analytics as an Engine Of R&D and New Product Launches
Predictive analytics is not only the way to discover the underlying patterns, but it can also help you with innovation. Here, we discuss the ways to innovate by combining it with business logic, marketing and bridging demand supply factors.
By Lana Klein, (Co-Founder, 4i)
Innovation is the driving force of growth, but many new product ideas fail in the marketplace, leaving a trail of wasted time and resources. Sometimes the product idea is good but is poorly communicated. Sometimes, the idea is not that good from the start or lacks proper validation before a go-to-market strategy is crafted.
Predictive analytics has been top-of-mind for marketers over the past several years. Yet, when it comes to innovation, there is often a lack of clarity on how exactly predictive science can fuel success of new product concepts. The key is in the granular approach to the idea in the context of its category, marketplace, and demand factors – breaking it down into components and using analytics to understand the concept’s evolution in the future.
Can Predictive Analytics fuel more successful Product Innovation?
Innovation and New Product Development have long been seen as a domains of creativity, trial and error, as well as just plain old good luck. Can Predictive Analytics tools replace some of the guess work and help companies create products with higher success rates? The answer is a resounding “Yes.”
So what do you need to create the Magic Innovation Crystal Ball?
Determine what type of products drive higher demand and why:
- Start by identifying key properties and attributes that describe products in your market category. For example, products in Food and Beverage categories are often defined by a combination of attributes related to taste, various health benefits, package sizes, and price tiers.
- Then find market data that informs you about historic sales of specific products; model this data to identify what types of attributes describe best-selling and fastest growing products
- Create algorithms that help to measure how much product success is driven by each specific attribute. Once you have done that – let your model suggest what combination of product attributes is more likely to generate higher selling products
For example, we worked with an oral care company in helping them innovate their product lines within the broader category. One of the key exercises of that project was to understand what’s important to consumers as they engage in the daily routine of caring for their teeth.
Those factors included cleansing effectiveness, look-and-feel (white teeth, appealing smile), feeling of freshness, anti-bacterial protection among others. Modeling these attributes and distilling the most successful combinations helped us design a robust product innovation pipeline for the client. The company later launched a whitening toothpaste, which has become one of the top selling brands.
While commonplace today, the concept was groundbreaking at the time. Yet, the combination of cosmetic appeal and cleaning function has proven to be extremely successful in oral care.
Consider what type of products you can supply and sell profitably
- Identify the attributes that are usually identified with your brand. How far away can you go from your “core attributes” and still be credible in the market? For example, if your brand is known for super-healthy but fairly basic tasting products, you are probably not going to be credible in the high–end indulgent taste market
- Consider the required margin for your firm and determine the type of products you can produce and sell profitably
Optimize Market Demand Factors against your Supply Factors
- Use the model to find the most attractive combinations of product attributes to optimize attractive market opportunity and your ability to execute
Another example is helping a personal care manufacturer introduce innovation in its shaving portfolio. The company’s footprint in multiple personal care categories of skin care proved to be beneficial in capitalizing on the existing white space of shaving and skin health convergence.
Identifying key attributes of the category and aligning the findings with the client’s operational capabilities, as well as brand positioning, allowed us to drive several successful concepts for shavers not only as hair removal tools, but also as skin condition enhancers. The innovation pipeline produced several successful product launches and generated over $100M in sales.
Combine Modeling Output with Business Logic
Modeling output provides a great starting point. Balance and test it with business logic to identify options that appear most reasonable, and focus you innovation efforts in these directions.
All that said, new product launches should not be devoid of creativity. The creative aspect is an important one: a new product has to resonate with its target audience. Predictive analytics, in turn, helps validate the creative process and provide the necessary data that would justify and back up design components, claims, packaging and other finishing touches.
Another crucial role analytics plays in the process is facilitating collaboration between multiple business units. When data is in the center of ideation, stakeholders from marketing to sales to R&D can jointly take part in decision making, align to corporate goals, and help increase the chances of the successful launch.
Bio: Lana Klein, a co-founder of 4i, leads the firm’s Growth Foresight practice. She is a recognized industry expert in developing unique client solutions combining advanced predictive analytics with deep business knowledge. Focused on the CPG and Healthcare industries, Lana has more than 20 years of experience advising clients on a broad range of analytics solutions.