It’s never been easier to create data in an organization. At the same time, it’s never been more challenging to create actionable insight from vast amounts of feedback.
Data is a source of advantage. Knowing more about the customer can only help, right? Data can be held anywhere: in CS, Web stores, retail, and of course, externally by retail partners. On top of this, there is a vast amount of feedback generated by the customers—for instance, reviews on Google or Amazon, Twitter, and Facebook interactions.
How do we best understand what the customer thinks? The solution has a number of steps with the aim of pulling all the data together to create a single view, then understanding how different subjects in the text relate to each other. The key to this problem is in analytics. In this blog, we will look at approaches to building analytic solutions for vast amounts of data both internally and externally, to transform the customer experience.
This is potentially a huge task and can often be complicated. There can be a significant cost involved in terms of resources but also time and energy in every company department. However, some ways don’t require as much pain and indeed can be very smooth. Many organizations choose to postpone their analysis or convince themselves that it’s not cored to their business. Consequently, they always end up missing out on insights that can transform their competitive landscape.
What are the benefits of improving feedback analysis?
Before you start, it’s essential to understand what your goal is and the potential benefits:
CX: The primary benefit is the improvement of the overall customer experience. It should go without saying that excellent customer experience improves brand loyalty and decreases churn. Being able to analyze across multiple customer touchpoints is the first step to understanding the customer experience as a journey rather than one of the individual interactions. The analysis is the first step to understand the customer as more than just data.
Product and service improvement: Understanding what customers think of products and services is an evident gauge of how useful feedback can be. Effective feedback analysis goes further. It can help you understand the relative importance of different features and how they interact with the product or service as a whole. It also enables you to see which products or attributes aren’t relevant, and even which ones customers use to compare you to competitors.
Net Promoter Score (NPS) improvement: Most will know that NPS is the single most popular customer benchmarking metrics used today. NPS surveys segregate customers into Promoters, Passives, and Detractors according to their feelings to your brand. If 50% of customers surveyed are Promoters while 40% are Detractors, your NPS is 50-40=10. A high NPS needs you to focus on far more than just a single transaction built on the entire brand experience. This means turning people into Promoters by accounting for every kind of contact. You can only do this by understanding all your customer feedback.
Top-line business growth: Although organic growth is important, every CEO wants to increase its cross-selling and upselling rates. Research from Gartner shows that collecting feedback can do this by 15% to 20%. Better customer experience also leads to lower churn, lower retention spends, and higher customer lifetime value.
All these benefits are described in our blog The power of 360-degree customer feedback analysis.
Why do you need a feedback analysis solution?
There are two principal drivers behind deciding to find feedback analysis solutions:
- The first is the realization that you may have vast amounts of data and either need to find a way to analyze all of it with the same tool or at least to find some insights which can be actionable. For many executives generating data is a method of creating a competitive advantage. The race to develop “Big data” too often leads to paralysis.
- Even as many organizations grow, their principal mode of understanding their customers is through survey data. Sometimes this can be using well-known metrics such as NPS, CSAT, or CES or perhaps something more sophisticated. Although these trackers can be beneficial, ultimately conducting surveys simply is not enough. A survey doesn’t tell you enough about what a customer feels or why and doesn’t tell you quickly enough.
Before you begin to draw up your plan to develop an analytics solution, you will start considering who will be building and running it. Many will choose to outsource the entire process though others will try to create something tailored for their organization. Before you start, you should establish a number of critical criteria which will help communicate the vision internally as well as drill down into what needs to be done:
How “core” will analytics be? Analyzing a large dataset should be approached with a clear strategic vision and an awareness of all risks involved as well as the planned benefits. There is a tendency to regard analytics as a back end or support function whereas for many organizations it can equally be considered as an integral part of the front end strategy
How much will this cost? At some point, the strategy will rely on data specialists, either internally or externally. There has been a severe shortage of data scientists in the US and Western Europe for the past few years, which makes it challenging to keep good people. Talent doesn’t come cheap. Neither does experience.
How quickly will we need this? Building a team up yourself takes time, and constructing your analytics tools will take extra time on top of this.
What skills do we need? Putting the right team together depends on the software that needs development. It also depends on the technology stack in use, how the new application or platform is going to interact and integrate with that stack, and the skills you’ve already got on-board.
What is the risk? When software and data are involved, the most significant risks include the potential theft of data and failing to comply with regulatory requirements (such as GDPR in Europe and PCI DSS). Quality control and data security will also be factors you will need to take into account here.
Balancing control vs. innovation. Generally speaking, external agencies can be more efficient and quicker as they are less likely to be slowed down by politics or processes. They can move faster and innovate quicker too. You trade all this for more control and ownership.
What culture do we have? It is necessary to think before the project about the culture in your organization. At a macro level, companies that aren’t customer-centric can find it hard to gather support behind efforts to understand customers better. Secondly, company attitudes to data-led decision making vary. Who are the end-users for the data? Marketing analyst? The CFO? Who will be running queries, and what relationship will they have to the user?
How to approach customer feedback analysis
Once you have decided to improve your customer feedback analysis, there are three choices to be made.
- Outsourced solution
- Insourced solution
- Saas solution
Many firms currently adopt a partial outsourcing strategy, whereby baseline, operational, analytical activities such as query and reporting, multidimensional data analysis, and OLAP are outsourced. In contrast, the advanced descriptive and predictive analytical skills are developed and managed in house.
However, there is no “right” way to go forward and much depends on your organization context and desired outcome, so below, we will outline the pros and cons of each.
This would mean giving the project and analysis to a third party to create and operate.
- Expertise: when an organization hires an external specialist vendor, its aim is primarily to employ extremely skilled people they can trust to deliver. Besides providing excellent analysis, they are also experts in identifying the actions the companies need to take to improve their businesses.
- Unbiased review: although external vendors will always come with their own biases and preferences, they are ultimately the best group to challenge conventional thinking on all assumptions within your organization.
- Speed: with most external vendors, you get what you pay for. This means that if it’s possible, work can be integrated quickly, and the analytics performed quickly. It also means the external vendor will manage stakeholders within your team and management to ensure the smooth running and completion and eventual handover of the project
- Easy access with your team: you and your vendor can decide the significant points of contact and the interface for discussion and sharing information.
- Costs: outsourcing an external vendor can ramp up quickly. Solutions will come with license fees, consulting costs, and typically per-seat license costs. Add into this the internal time and energy in managing an external supplier. Many decide the cost-benefit analysis may not be viable.
Analyzing multi-language customer feedback in large volume from different sources is a complex process. With an AI-based technology and years of experience, Wonderflow is helping global brands to become customer-centric. Find out more about our solution.
This usually means hiring your staff to build a solution from scratch.
- Doing it yourself can offer greater control, and to some extent, less compliance risk (especially if you are accessing your data).
- As an internal resource servicing the analytics needs of the wider business, it can be assumed that you would have a deeper understanding of how the company operates, along with its business processes and systems, which should enhance any project.
- Projects and internal resources can be internally prioritized to meet the demands of an initiative; however, sometimes, politics can come into play here.
- Very often, this approach is more expensive than hiring an external data analyst or agency, diverting resources that could be used to improve your product or service.
- Many companies lack the practical in-house knowledge and experience needed to put together an analytics team.
- Your data science team will very likely not be able to identify the actions that the company needs to take unless they have board-level representation.
- There is a high cost associated with attracting and retaining high caliber analytical professionals in-house.
- Without data and insights that every employee can access, your company won’t have a data-informed culture. More often than not, internal analytics resources are time-constrained, find it challenging to keep up with ad-hoc reporting and analytics requests from the business and can be moved from one priority to the next. Ensuring you have the relevant skill as well within your team for the job at hand is essential.
A Saas (software as a service) solution is a service developed by another company that your organization can buy. There are Saas solutions that focus on getting actionable insights from customer feedback.
- Speed: once you have the right solution, it can be set up in a matter of days or weeks. The data will be instantly analyzed and turned into reports.
- Low maintenance: software is always updated with new features, so you don’t need to maintain it. Additionally, the value of the software is constantly increasing because technology improves with each update.
- Data concerns: you need to rely on Saas vendors’ security policies to make sure that your company’s data is safe. Look for internationally recognized certifications such as SOC2 and ISO-27001.
- Costs: some Saas solutions can be very expensive but not scalable.
The cultural impact of your choice
It’s easy to think that understanding customer feedback is a closed-loop task with a specific beginning and end. This is true in project management terms, but at Wonderflow, we always understand that the success of customer feedback and analysis projects are dependent on company culture and can often create change in culture too. Usually, it’s the first step in becoming customer-centric and developing processes and systems designed with the customer journey in mind.
Because we encourage open access to our analytics in the organization (training for the Wonderboard is very simple), data culture is influential too. With the right analysis, it’s easy to understand how changes in customer experience can impact individual as well as company goals.