How to Use Data to Deliver Better Marketing ROI
Data – the evolving situation
When we talk about data in today’s world, we are essentially referring to any information that is translated into a digital form that can be transmitted or processed. But what do we mean by Big Data? Bear with us whilst we quickly get the nitty-gritty out the way…
Big Data is made of four key principles:[/vc_column_text][vc_single_image image=”1444″ img_size=”large” onclick=”img_link_large” el_class=”blogImg”][vc_empty_space][vc_single_image image=”1447″ img_size=”large” onclick=”img_link_large” el_class=”blogImg”][vc_empty_space][vc_column_text]
“Generating structured upfront data sets can improve campaign efficiency but manually aggregating and managing so much data can prove a real challenge.”
We throw the word ‘data’ around willy-nilly and it struts around the marketing team looking very self-assured.
But data is nothing without analytics. It would be like Laurel without Hardy, Ben without Jerry, Jobs without Wozniak…you get the idea. Given that around 80% of available data is unstructured, there is a two-tier job here.
One is to quality check the data and ensure it’s usable, and the second is to interpret this data into something that will result in a tangible benefit. This might take the form of customer loyalty metrics, engagement measurements or of course our personal favourite: performance optimisation.
But why is this at all relevant, and why now, you may ask, when there has always been ‘data’?
Well, it’s estimated that 90% of the world’s data seems to have been created between 2014-2016, with 2017 having topped ALL of this combined, and thereby creating more data than the last 5000 years combined. It’s a good time to start paying attention.
The figures don’t lie – why data matters
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But who is going to take responsibility?
The other question is about where data management should sit. Some businesses separate out the data function into a dedicated team that operates under names such as ‘Management Information’, ‘Data Insights’ or ‘Business Analytics’.
In principle, this is often a commendable reflection of a company’s vision to dedicate resource to implementing data into decision-making, but it’s risky unless that particular team work very closely with marketing, otherwise you end up with reports that don’t necessarily reflect business performance, but look rather lovely in PowerPoint.
Given that the data revolution has only really taken off in the last few years the sad reality is that many CMOs aren’t particularly knowledgeable about the technology or methods with which data is used to drive business growth within the context of digital marketing.
Many a marketer would be telling tales if they wouldn’t admit to aggrandizing empty metrics at least once in their careers. Here is where we can amalgamate the strength of an ROI-focused CMO, used to tracking sales, with data from digital channels and marketing in order create robust reporting.
This can be further evolved by stepping away a little from CRM data to integrate it into the wider picture of the businesses’ online performance and then how this macro perspective can be used to optimize campaigns.
To go further and state the obvious, if your pillars of marketing, finance and sales are not operating in symbiosis then it’s time to go back to the drawing board.
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Three examples of where data has inspired success
1) Tapestry Inc.
Tapestry Inc., the New York-based house of luxury fashion brands, has centralized its data analytics, pulling together data from three formerly independent companies (Coach, Kate Spade New York and Stuart Weitzman) and combed this with all the data from the various marketing channels being used by the business.
The company is recently formed, and the news that C-suite prioritized the collection and organisation of data in the first instance has produced very impressive results which set a strong precedent for other companies who’ve undergone mergers.
Tapestry’s Data Labs team created customized tools that enabled them to access and work with the varying data streams, and we know from experience how difficult this can be, so a prolonged nod of respect is in order.
Successfully implementing a multi-channel view will be invaluable to decision makers, and we looked forward to hearing news of the brand’s progress.
Machine learning is continuously pushing the boundaries of what can be achieved with Big Data and Alessandro Magnani, Data Scientist at Walmart Labs, has explained how the company is using machine learning in product recommendations.
Walmart is an example of a company that has started integrating semantic search* in the form of text analysis and synonym mining to improve their customer’s search experience – leading to a greater accuracy and a consequent increase in conversion rate of 10-15%, which is significant for a business with over $500 billion in total revenue ($11.5 billion of which comes from eCommerce sales).
*Semantic search allows search engines to interpret natural language terms rather than the small sets of keywords we usually see, and marketers are starting to take account of this in their site search functionality in the ever-changing SEO game.
3) Maggi (Nestlé)
“Here at Filed, we’re naturally inclined to getting excited when we see instances of synchronisation between Artificial Intelligence and Big Data.”
We were therefore very impressed with ‘Kim’, the Maggi chatbot developed by Nestlé Germany.
Here we see a direct benefit to individual customers, since Maggi creates meal suggestions catered to individuals and then helps them order and cook the ingredients.
This is a huge selling point in terms of improving customer experience and brand perception, and as you’ll see from our comments on Accenture’s recent report below, using personal data to create relevant content is essential for businesses to succeed.[/vc_column_text][vc_empty_space height=”70px”][vc_column_text]
No data? No business.
Not only do we see failure due to a lack of data, we see both failure and mistrust when it comes to purchasing systems that promise a solution to all your data woes but end up being more of a burden than a benefit.
This is usually due to a lack of usability, a complete absence of any meaningful customer support and a lack of training for teams who might be rather under experienced with both analytics and related technologies. Alas it seems that the purchasing department’s policy on cost-effectiveness is not always practical.[/vc_column_text][vc_single_image image=”1454″ img_size=”large” el_class=”blogImg”][vc_empty_space][vc_column_text]Market research, often relegated to a perusal of a few competitor newsletters, can be make or break for many businesses. This might be due to introduction of a poorly placed product, unrealistic pricing models, or misunderstandings about market sizes (amongst a range of other pitfalls).
Businesses with a clear understanding of their target audience who invest and commit to using CRMs are in significantly stronger positions than those that do not. This is especially true of digital marketing.
“After all, how can you target your customers effectively if you don’t have accurate data sets on them?”
Not investigating the marketplace is often cited as the #1 reason new businesses fail.
Data is key to scalability. Whilst 40% of SMBs are profitable, 30% are also constantly loosing revenue. Of the myriad reasons this happens, where we’ve seen businesses plateau, it’s often been because they are attempting to expand without effectively capturing and using data.
For B2B this can be particularly challenging, as anyone who has ever tried to convince a sales team to input everything on to their CRM will confirm. Sadly, developing without knowing where your customer touch points have occurred is a sure-fire way to shoot yourself in the foot.
CRM and customer service goes hand in hand, and with 78% of customers abandoning intended purchases due to poor customer experience, this is a core functionality of data that cannot be ignored.
This month, Accenture announced that their research indicates that US companies risk losing $1 trillion if they fail to maintain customer relevance.
Rather damningly, Accenture’s report revealed that 61% of consumers switch companies that do not meet their needs, with 44% experiencing frustration when their personal data isn’t used to make offers and interactions more relevant.
As we know from the first case study you’ll see on the failed start-ups list, Fab, pushing the wrong content and products at your customer base can backfire, and spectacularly so. Accenture recommends 5 core actions needed to transform:[/vc_column_text][vc_column_text]
- Target core and disruptive growth initiatives.
- Design products and services as hyper-relevant platforms.
- Build a range of engagement channels through agile technologies and prototyping.
- Scale a broad set of ecosystem partners.
- Rewire the workforce through new technology and a culture of hyper-relevance.
We’re struggling to see how anyone could implement the above without a robust data strategy.[/vc_column_text][/vc_column][/vc_row][vc_row el_class=”blue-box”][vc_column][vc_column_text el_class=”blue-box”]
How to implement data into your business strategy
- Centralisation. Start by mapping out your data sources, then put resource into consolidating this data in one place. Synthesise & clean data sets and implement a data quality assurance processes early on.
- Customer lifetime value (CVC). Tackling your data (or lack of data) can be a daunting process. Begin to review your customer lifetime value, also referred to as Most Valuable Customers (MVCs) to help you prioritise your workload so you prioritise leads with the highest income potential. Managing data can involve significant human and technological resource, after all.
- Integrate your social media data NOW. Without this, you won’t be able to optimise your social campaigns. Our handy guide to the Facebook Pixel will get your started.
- Make use of what is freely available to add data to your market research. Think public censuses, government reports and whitepapers, and free online tools like the rather undervalued Google Trends.
- Review your decision-making processes and identify which key decisions could be made more accurately by being informed with data-backed analysis. This will help prioritise what data you need to focus on.
- Optimise your sales-rep outcomes by matching data-informed MQLs with your most experienced sales reps to improve your conversion between digital marketing and sales.
- Consider your customer needs against your competitor offerings and use data insights to plug these holes to offer the most relevant solutions.
[/vc_column_text][/vc_column][/vc_row][vc_row][vc_column][vc_empty_space][vc_column_text]One could easily dredge up the age-old adage that ‘knowledge is power’, except this time, ‘data is power’. We could come up with any number of analogies involving marketing without data is like X, but we will refrain and wait to hear about your successes, rather than see you on the next ‘why businesses fail’ list.[/vc_column_text][/vc_column][/vc_row]