A 2018 DOMO study has shown that we produce 2.5 quintillion bytes of data every day. The same study estimates that by 2020 every person on the planet will produce 1.7 Megabytes of data per second. If you can’t quite wrap your head around these numbers, that’s ok. Neither can I. But here is something important to remember. All that data being generated is either public or voluntarily shared with you in the form of loyalty cards, website cookies or email signups. Now some data might be private, but most of it is accessible. Most importantly, users decide themselves what they want to share with you as a company and what they do not. In exchange, they expect better products, services, prices or personalized experiences.
Data in Marketing:
Why should you as a business owner or marketing leader care about integrating data into your everyday decisions? A 2017 McKinsley & Company study revealed three persuasive statistics: Companies that use data in their planning and decision making, see an 85% growth in sales, 25% increase of gross margins and a 6% increase in their profits, in comparison to their competitors who do not utilize data. In this blog we will look at 3 distinct marketing responsibilities that benefit from customer data insights.
Improving Customer Experiences:
Using a holistic view of your customer’s journey can impact multiple touch points. Knowing how they research, review, shop or involve customer service, will help you update your web and social media presence as well as advertising strategies. It will help your business refine your customer service SLAs and processes, and so much more.
It will help you understand what stage of your customer’s lifecycle they are in before offering email discounts or queuing social media ads. One of the strongest tools in a marketer’s arsenal today is to create customer experiences based on real and accurate data.
Since modern customers no longer follow one linear path to purchase, it can be difficult for marketers to create a nurture journey accounting for each possible customer action. Many of us switch devices, laptops and cell phones or even use a VPN enabled devices. Companies may lose track of individuals across multiple platforms, devices and IP addresses. To add to this, companies themselves have chosen to be present on multiple communication channels, such as social media, emails, text, websites and so many more. All these factors add to the complexities of customer journey mapping and personalized content offerings.
Gone are the days of sending emails with a customer’s name, address or company information were impressive.
A 2016 Salesforce study declared that 52% of customers are very or moderately likely to switch brands if the company does not extend personalized offers. That is scary for marketers. Data analytics is not easy to come by in our world. Multiple hurdles must be overcome to make such holistic customer insights possible. Complexities include customer activity tracking, obeying data privacy laws, the price of a data management technology and of course the cost of talent.
Another business activity that can be impacted by existing customer data is the acquisition of new customers. It is no secret that retaining existing customers is a lot more affordable for a company than finding and on-boarding new ones. Some studies say, new customers can be up to 5 times more expensive than keeping customers loyal to your brand.
Imagine a world, where your marketing team using prescriptive data analytics, could profile your most profitable customers. What if your marketing team could laser focus their work on leads that have the most attributes in common with your most profitable customers?
We have seen repeatedly that niche marketing, and hyper focused targeting, while reducing the size of our targetable audiences, yields higher conversions and reduces lead generation cost. So, while your pool of potential clients may downsize, the conversion rates and efficiencies of your nurture journey may skyrocket.
In extension of this concept, you may also apply this methodology to find customer attributes for those who have been most open to expanding their portfolio with you. These characteristics will then lead your cross- and up-selling marketing practices. Can your sales team use these insights to create better profit margins for the company? I believe so.
Marketing Mix Planning:
Business owners and marketing managers constantly struggle with tying increased revenue or lower operating costs to specific actions taken. Whether they are internal efficiencies in operations or external advertising changes. Knowing exactly which action drove what impact on sales, costs or customer satisfaction is a must for every company. Yet, it is one of the hardest things to do. It requires a multitude of data sources to relate to each other, or at least flow into the same data warehouse.
Such centralized data storage is often called a Data Lake. But just getting all the data into one “lake”, does not provide insight or measurable ROI calculations. Once all the enterprise data sources are in one place, you need to apply data analytics processes to tie specific actions to measurable results. In the long run this process will permit the business unit leaders to make better resource allocation whether it is a headcount, time or money question. Advertising managers can choose the best performing communication channels, graphics, text, tone of voice etc. to drive the biggest impact on ad campaign.
In marketing terms, we often speak about revenue attribution to specific campaigns or marketing activities. Learning from best performing and converting content, marketing teams can turn off inefficient ads, and re-allocate budget to top performers for better results.
Real World Challenges:
If there are so many benefits to using data analytics to support marketing why are so few companies doing it? Even more so, why are companies who have implemented a Data Lake still not seeing those magical results as measured by McKinsley & Company?
The concepts described above are a utopian vision of data use. The real world has many constraints to face. As a marketer who works with data everyday, the most typical issues I have seen range from a comprehensive tech stack, to a lack of talented resources or a silo-ed approach to using data.
Let me expand on these for a moment.
1) The technology needed to create and maintain a Data Lake has been expensive and difficult to implement. It is also slow to fully mature in the past decade. It is not just the storage technology that is expensive, but the connective tissue and technology needed to bring all the enterprise data sources together is also expensive.
Think how many teams in your company have access to customer data? Do they all work in the same software? I doubt it. On average companies use a CRM system for sales, a marketing automation system for marketing (and a social media listening tool for social media ads and customer care) and of course there is a customer service software for customer issues. (We haven’t even thought about Operations, Customer Satisfaction or Customer Research databases, Accounting databases and so on.
To give you an example, I know a company that has 9 distinct CRM systems, one email marketing platform, 3 social media data streams, 8 customer service systems (and don’t get me started on operations). Just to create one single source of truth is overwhelming both from a technology cost, time and resource intensity perspective.
And even when the data is finally in one place, you still must clean it, parcel it and make sense of the trends perceived in the data before it can apply to a decision-making process. And that takes me to my second point:
2) Lack of talent. If you think buying and installing the technology is expensive then keep on reading. Because finding a good and experienced data scientists is very hard. (becoming a data scientist is a fairly recent career path) And because they are so scarce, they are also very expensive. Small start-ups and small to medium sized companies cannot afford them, or at least not very many of them.
And then there is the skill problem, true data scientists are extremely technical. They do not speak the language of the business. Don’t be fooled, a data scientist can’t tell you what question to ask. You need to be guiding the data exploration. Data scientists are great at navigating and managing the data. But as the business representative you need to clearly define what question you are looking to answer. Once they bring the data to you with insights, it is upon you and your business knowledge to turn it into a decision in your field of expertise.
It is important that as a business manager to clearly define what business problem or hypothesis you are trying to prove or disprove. What are you trying to find an answer to? If you don’t know that, even the best data scientist with the cleanest data cannot fix your problem.
3) Lastly: Silos. The lack of enterprise wide buy-in, into a data driven decision process is the downfall of any project. You will not get a holistic customer view if all you do is provide partial data to a data scientist for processing. Sometimes even tearing down data silos within the company won’t give you enough data to work with. In those cases, the company must be ready to spend the money to acquire additional data. That way they can augment existing sources and enable accurate insights and business decisions.
I have spent the last 3 months personally on a data integration project. We are combining social media advertising data with email marketing performance data to create a better view of our customers. The end goal is to create a lead scoring process for our marketing funnel and to create an omni-channel customer nurture journey. This will span text, email, web and social media.
While I am excited about our data integration project within marketing, I also believe that trying to take on an enterprise wide project in one go is doomed to fail. Taking an iterative approach of integration, tech stack build-out and data governance processes is the only way to not break the company in the process of innovation. Even small, incremental insights can help companies be customer centric and find efficiencies.
Start with small steps. Have utopia in mind to keep teams rallied towards one common goal, that of a customer centric approach.