Join us at the upcoming IIeX 2020 where we will present a number of trending insights ranging from using website replication to optimize portfolio offerings, making use of voice analytics to gain deeper insights for new product development, and combining Conjoint + Time Series technique to understand consumers’ preference and assess impact on market share when changing or introducing products/services.
The Online Overload: How VodafoneZiggo optimized its portfolio by better predicting consumer choices online
In today’s highly competitive telecommunications market, consumers face an abundance of choices online. To thrive in this environment, an operator’s product portfolio strategy should be optimized based on how decision-making is changing. You need to know how customers identify the best carrier and plan for their needs. And that’s where the most accurate customer and market insights can help.
Marketers realize shopping decisions and price comparisons are often made online. However, understanding, and accurately predicting, exactly how shoppers find and choose products and services online has been difficult. Such was the challenge VodafoneZiggo recently tackled as it needed to differentiate itself in a maturing market.
To overcome these challenges for companies competing online, we developed the new Filter CBC Solution to replicate a price comparison website. Filter CBC expands on traditional conjoint, analyzing consumers’ product choice reasoning, offering attribute variations (e.g. price, data etc.). However, it gets closer to reality for respondents by letting them see – and then filter – over 100 possible options. This research technique delivers more realistic results for more accurate insights.
In this collaborative presentation between VodafoneZiggo and SKIM, we will show how the new research approach works, how it can be applied and how the results have helped VodafoneZiggo’s business success.
Jan Zwang, Head of Market Inisghts – VodafoneZiggo
Mining emotions for deeper new product development (NPD) insights: Can smart innovations save us from stress?
Qualitative research allows us to look through ‘the eyes of a consumer’ and understand the emotions and attitudes behind decisions. However, collecting a robust sample of consumers’ emotions at a large scale remains a challenge.
To tackle this challenge, SKIM has innovated a new hybrid approach for early-stage NPD research. By using a voice analytics tool, we can analyze ‘how’ people communicate their needs, attitudes and interest, to better uncover implicit emotions for more effective innovation strategies.
In cooperation with Johnson&Johnson, we ran a case study in an emerging consumer and healthcare market: personal stress management
Data Fusion: Conjoint and Time Series
Before introducing or changing any products or services, the brightest minds in any business will put time and effort in assessing the impact of such products or services on profit or market share and understanding customers’ preferences. While customers’ preferences might give directional information on the market share, the former unfortunately is not equal to the latter.
One of the methods in specifically assessing preference is conjoint, which is a discrete choice modeling technique that uses trade-off tasks in which respondents reveal their preferences towards the concepts or products and individual elements thereof by choosing between different product offerings. On the other hand, the traditional time series forecasting can be used to predict market share using revealed preference data such as sales, distribution, and promotion.
The problem of using each method in isolation is that one cannot rely only on stated preferences or only on historical data to make an accurate prediction on sales when launching a new product or a new product feature. Our approach to solving this problem is to perform a fusion between the key results of conjoint analysis and time series forecasting. This combined model gives companies the tools to play with all relevant factors in one tool while having a more stable model based on current preferences and historical data. In my presentation I will show what techniques are used and what considerations there are when merging both worlds.
Date and time: TBD