We live in an era where customers expect an ever more personalised customer experience. And so they should. With the increasing number of touchpoints between companies and customers and the information that we’re able to generate and capture from those interactions – we have the data to be able to provide that experience. The issue that many businesses are facing is that their data is siloed across different platforms and departments making it near impossible to take advantage of the potential of this information and obtain a single view of the customer.
Cloud for Marketing (C4M) allows businesses to take their siloed data from those disparate platforms and traditional data warehouses and securely bring it all into one place. To integrate, collect, analyse, and visualise the information so it can be more simply understood and used. Meaning that you spend less time trying to connect your data and more time learning from and activating it responsibly.
As it’s a concept still relatively in its infancy for the wider marketing community, we’re going to showcase the benefits of cloud for marketing for each part of the marketing process with example use cases and a couple of case studies too. We’ve narrowed it down to 3 steps for how we can use the cloud to improve the customer experience which will form the basis of our Cloud for Marketing Blog Series.
1. Understanding the customer journey
2. Predicting marketing outcomes
3. Personalising customer experience
Understanding the Customer Journey
As you already know, the customer journey is compiled of every single experience and interaction a customer has with your business or brand. From the initial Google search for retailers through to your website, evaluating its ease-of-use, through to the transaction screen, and inevitably the follow-up.
But why is it so important that we understand the customer journey? Well, for starters, attracting your customers’ attention is becoming increasingly difficult. The digital landscape means customers are no longer restricted by geographical location, and the market is vast. Being seen presents enough of a problem on its own.
Even if you’ve managed to attract customer attention, a lot of companies are struggling to use the data they have effectively to forge stronger customer experiences. A customer may make a transaction, but the lack of convenience in the process may stop them from coming back.
Another common issue that’s impacting on the quality of the customer journey is the lack of infrastructure set in place to manage and capture the large volume of marketing data to ensure consistency. What’s more, when it is captured, it’s too late to do anything about it. The relevant customer experience and messaging isn’t reaching new or existing customers at the right time.
So now that we know why the customer journey is so important, do we start to address these problems? By driving insights from data. And how do we do that? With Self Service Analytics. We need to collect and integrate the data better in order to better analyse and visualise it.
Self Service Analytics
Self-Service Analytics can help understand the customer journey, analysing how they arrived at each point of interaction with your business. Business Intelligence (BI), which gives end-users the ability to develop near-instantaneous reports and analyse their data
Self-service analytics is simply for non-technical users to interact more straightforwardly with their aggregated data, even creating parameters and customising reports. This all takes place on the dashboard. It’s a sort of shareable workspace where all data is compiled, helping generate insights and aid decision-making simply through making the data visible. With this dashboard, you can reduce time squandered on transactional reporting with stakeholders and provide easier access to the data required.
Personalising the Customer Experience
To use a case study example – for a travel brand operating globally across over 20 countries with more than 40 brands aggregating data for insights was no easy task. They had no feasible way to provide data-driven marketing at such a scale. The solution was to use the cloud. By taking hit-level raw data in Analytics 360 from across every website and app, and compiling it in Marketing Data Store, they suddenly had access to an unprecedented level of marketing intelligence.
With this setup the company was able to share data-sets across employees of different functional expertise, so they could all work towards the same goal. The intelligence wasn’t siloed in hard drives, so could be used to assist marketing across the company’s brands, and they could create customised dashboards to inform campaign intelligence. All of this was possible within a reasonable timeline, meaning campaigns could be adjusted in real time. In all, the brand aggregated almost 3 terabytes of data per week.
The customer experience is not what it once was. One size no longer fits all when it comes to marketing, which could be a technical nightmare, tailoring journeys based on individual data sets. But that’s why C4M is so important. By aggregating raw data into a user-friendly dashboard, self-service analytics makes data and insights accessible when you need it, how you need it.
Having access to data is important, but it’s just the first step. Without the right tools, companies waste valuable time and web developer resources trying to share and understand it.
Stay tuned for part 2 of our Cloud Marketing Series, where we will discuss how to predict market outcomes. If you have any questions or want to get in touch with the team at Datisan hit us up here.