How to use qualitative data to improve customer journeysby
Customer opinions are given most of the time as qualitative data (soft data) and not as quantitative data (hard data). But it is possible to convert soft data into hard data to facilitate analysis.
The key to successful multichannel marketing seems to be a focus on managing the customer contact points rather than the channel. A channel focus assumes that the company owns the customer experience, when in a multichannel environment the customer owns the experience.
Respecting customer channel choice includes things like arrangements for returns, exchanges and customer service issues and the understanding that all the changes in consumer shopping habits are being fuelled by the rise of smartphones, tablets and social marketing interaction. Data indicates that the average shopper uses an increasing number of sources of information to make a decision - Shopper Sciences found that the number doubled from 5.3 sources to 10.4 between 2010 to 2011 alone.
These numbers show how difficult it is for brands and retailers to grab customer’s attention in the first place. People’s opinions about retailers and brands are given most of the time as qualitative data (soft data) and not as quantitative data (hard data), but it is possible to convert soft data into hard data to facilitate analysis. Customers are constantly connected and following non-linear journeys when making the decision to buy.
So how do you create a framework that includes qualitative data in customer journeys?
Step 1 – Build trust with your customer
The consumer trust in an online retailer is a significant predictor of perceived internet confidence and search intention for product information via the online retailer. Consumer trust is a crucial factor for successful business trades, and in turn, the development and management of a long-term customer relationship. When consumers have an existing trust in an offline store, they are willing to purchase the products online, willing to spend more time at the trusted retailers’ website and willing to recommend the same online store to others.
Step 2 – Think about product attributes and not about product price
Consumers not only compare prices but also compare product attributes offered within an online retailer or by different online retailers. Consumers may spend more time at the online retailer to explore alternatives, fulfil utilitarian needs and/or intrinsic motivations. To increase online sales and consumer trust level, it is vital for retailers to provide these online shoppers with what they seek in the online store in a timely fashion, because most online shoppers are goal oriented. To fulfil these consumer needs, retailers need to provide accurate, detailed representation of product information and timely responsive customer service.
Step 3 – Transform your qualitative data
When doing a qualitative analysis one key aspect is to transform the data into a quantitative structure to facilitate interpretation. By using RACE digital marketing improvement framework and the best practices from Google’s ZMOT framework for multichannel success the three curves framework can work as a starting point to set up a routine business process that analyses qualitative data such as comments from reviews and social media.
These are examples of techniques you can use at each stage if you decide to implement this three curves framework.
- Reach curve – You can get insights by telling stories using social media networks. You know that consumers will be trying to discover more about you, searching for solutions and researching both you and your competitors. If you create an # on Twitter to discuss interesting subjects for your customers you may be giving them enough information at this stage. The #ecomchat is a great example of how you can get the conversation going in a controlled environment.
- Engagement curve – Insights at this stage may be provided by a map of behaviour on site. It is very useful to create personas and the user stories that each persona will be performing on the website. This is the type of analysis any commerce system implementer must do when scoping the website (frontend and backend).
- Loyalty curve – Between the get help phase and share, the consumer makes the decision to be happy or unhappy with what was bought so it is important to have a good customer service and software that collects reasons for the call. If you have a list of complaints you can then attribute a score to each one according to the categories they belong to.
Not all clients share their experiences though, regardless if they are good or bad. For the ones that do share, the cycle begins again, between the share and discover phase, although now with more knowledge. The consumer in this stage is capable of advocating his own opinion based on experience.
What framework is your organisation using for qualitative data? Please share your thoughts about this and any case studies you can think of.