First, what is a Customer Data Platform (CDP)?
A customer or consumer data platform (CDP) is a platform or a software that creates a persistent, unified record of customer data that is accessible to other systems. Data is integrated, contextually enriched from multiple channels or sources, to create a single consumer data profile. This integrated and structured data can then be utilized by other marketing systems for various use cases like bid optimization, personalization, segmentation for ads and emails etc.
According to Gartner, “Customer data platforms have evolved from a variety of mature markets, including multichannel campaign management, tag management and data integration.”
So, then, why is customer data integration vital for insights-driven businesses?
Gone are the days of spray and pray marketing to promote businesses. Big data was a buzzword more than five years ago, but it continues to be relevant for business success today. Offerings from customer data insights have evolved into multichannel campaign management, tag management, identity resolution etc.
Consumer digital footprints are now larger than ever, giving enterprise businesses significant opportunities to target, market to, and maximize the lifetime value of customers. Businesses that take full advantage of big data and consumer data insights to meet these goals are uniquely positioned to stay ahead of the competition and thrive in the long run.
A recent Forrester report calls companies that focus on the value of data “insights-driven businesses”, and they’re set up to dominate the competitive market. Those that prioritize data insights are growing at an average of more than 30% annually and will earn a projected $1.8 trillion by 2021.
How to Build an Insights-Driven Business Culture
A lot of what empowers insights-driven businesses is technologies that allow them to process and analyze large volumes of relevant data at scale. But that’s only one piece of the puzzle. Successful organizations also develop a business culture where these insights are maximized and constantly used to inform decisions and improve processes.
Most enterprise businesses today know the value of big data and consumer insights, but few are prepared to make all the necessary changes to fully benefit from it. According to a recent global research report by Cloudera, 69% of enterprise organizations view having a comprehensive data strategy as a requirement for meeting business objectives, yet only 35% think their current analytics and data management strategies are sufficient for this purpose.
Choosing the right management platform for your business needs is valuable, but there’s a lot more to building an effective enterprise data strategy today.
The Keys to Building an Effective Enterprise Data Insights Strategy
Investing in technologies, such as QuanticMind that can integrate, manage, and analyze the wealth of relevant business data out there is the first step towards success. In order for enterprise businesses to maximize the value of major data analysis initiatives, they must also create an internal strategy for success.
This includes a clear vision for what the business hopes to gain from data analysis and a roadmap for achieving this. Key players at different levels of the business, beyond marketing or data science teams, must have a vested interest in this strategy.
Illustrating clear goals for your data analysis initiatives impacts your approach significantly. Here are some of the many ways it can inform your strategy:
- Illustrating exactly what data sets you need to work with, and identifying potential gaps in data sources. This allows users to assign real value to the different types of data you collect.
- Informing how different groups of data should be defined and referred to.
- Helping teams identify which past data management strategies and systems are still relevant or obsolete.
- Informing how users should approach data quality management, to minimize inconsistencies and fill in gaps.
- Allowing you to effectively eliminate irrelevant data, thus avoiding wasted time spent working with data sets that aren’t relevant to your goals.
- Enabling decision-makers to make informed choices about which data to use, how to use it, and for what purpose.
Some of the goals you define may depend on your type of organization and industry. That said, most businesses want to use data insights to inform strategies that can help them reach new customers, earn their loyalty, attract investment, beat their competitors, and improve other business capabilities.
Building a Data Integration Strategy
Once you have a clear understanding of what you hope to achieve with data analytics, you’re ready to identify which strategies and data management solutions you need to succeed. The types of data you need will depend on the different analytics goals you set out. Some examples include:
- Audience on-site behavior
- Audience behavior across the web
- Financial data
- Sales data
- Operational data
- Deep funnel data
- Offline sales and revenue
- Other company data
- Competitive landscape data
- Cost data from publishers
- Historical revenue and performance
This is the area where choosing the right data platform is most important for insights-driven businesses. There are plenty of data management solutions out there that can automatically collect, process, and analyze some of the important data points businesses need to meet their analysis goals. But missing even one or two of the key data streams means missing out on important insights or wasting time and resources with manual analysis.
Insights-driven businesses should focus on finding a data management platform that offers the most integrations to process and analyze all relevant data for their business goals. Either that or be prepared to invest in a partial solution and manage the rest manually with internal data science teams.
Defining Data Management Roles
Once you know what systems and technologies you’ll use to manage your data, it’s also essential to define clear data management roles. Not everyone is a data scientist, and many people outside of the analytics team will likely come into contact with and/or edit your enterprise business data. In order to maintain data integrity and management efficiency, it’s important to define key roles so everyone understands expectations related to data management within the business.
Identify all individuals who may perform some tasks in your data science pipeline. You may discover that internal team members don’t have enough skills or the capacity to meet analysis needs. In this case, it can be worthwhile to enlist the help of third-party agencies to work with your data and analysis tools. This is a common strategy among enterprise-level businesses – in fact, spending on marketing agencies accounts for nearly a quarter of marketing budgets today, according to the latest Gartner research.
Once you know exactly who will be interacting with your business data, you next need to detail what kind of tasks they will perform with it. When enterprise businesses actually take the time to do this, it’s easy to find ways to improve data analysis efficiency and performance. Your data scientists, for example, are an incredibly valuable asset for deriving insights, despite what automated technologies have to offer. Often, they spend significantly more time (as much as 80%) preparing data than actually performing analysis. Why not delegate some of the more tedious tasks to data managers, allowing data scientists to focus on the work that matters most?
Breaking Down Silos
In enterprise-level businesses, there are often issues with teams working apart from each other. This is true both within data management and IT tasks, as well as with the rest of the business. Insights-driven businesses need to make specific changes to their organizational structure to sidestep this issue. For starters, avoid allowing your data scientists to work in silos. It makes sense to assign different teams to work on different goals, but they should still have an integrated approach.
Ensuring clear communication between IT and business leaders is critical for success. Creating and approving clear data management goals is just the first step of this collaboration. There needs to be ongoing feedback between different units to ensure data insights remain the most valuable to current business needs.
Many business leaders believe that data should only be accessible by the teams that analyze it. But making it more widely available to invested teams throughout the business can maximize its value for reaching business goals in the long run. Breaking down silos may cause some confusion in the short term, but the long term benefits are worth the effort.
Reporting on Actionable Insights
Once a business is certain that technologies and teams are set up to maximize the value of big data, they need to ensure they’re prepared to report on data insights. How you approach this is largely based on previously outlined goals. Several important areas include:
Create reports that help improve operational efficiency and effectiveness. Relevant data insights should be made available to key players across the organization. Encouraging this kind of data-driven company culture makes it possible for all department leaders to improve internal operations based on these insights.
In today’s highly competitive digital landscape, efficiency is the factor that can set an enterprise business apart from its competition. Data insights can help a business identify problems and take action to streamline internal processes. Success isn’t just about improving marketing performance. Data insights can be applied to sales, customer relations, product distribution, employee performance, and more.
Marketing Investment Insights
Marketing investment insights are probably the biggest opportunity to drive more revenue from data insights. Campaign performance data can tell a business a lot about what strategies work and which don’t. Bidding technologies such as QuanticMind are uniquely positioned to help businesses automate changes based on these insights.
The time and energy marketing managers save by using automation needs to be reinvested into other initiatives. Evaluating the ROI of different marketing channels, exploring new options, experimenting with targeting strategies can lead to new data insights that improve performance even further. Insights-driven enterprise businesses need to be prepared to use both automation and manual data insights to inform future marketing investment decisions.
Data Investment Justification
Advanced data analysis can require significant time and financial investment, between collecting, processing, storing, analyzing, and taking action based on insights. Enterprise executive officers today understand that data insights are necessary for success. But they also need a clear picture of the benefits if they’re going to continue investment in the long term.
That’s why from the very beginning, there needs to be analysis processes in place to illustrate how this investment relates to the business’ bottom line. Linking data insights to drive specific marketing goals is one thing. But what’s the direct impact on sales? How does investment balance out in terms of ROI?
Systematic, Scaleable Action
Another critical aspect of running an “insights-driven” enterprise is that the insights truly drive action. There needs to be a company culture in place that ensures data insights are utilized continuously throughout the organization to improve processes and meet internal goals.
What makes this happen are teams ready to act quickly on insights to create a competitive advantage. But in 2020 and beyond, success is also largely based on automation technology. Look at the search advertising landscape, for example. Opportunities to optimally target search audiences exist in what Google calls “micro-moments.” Consumers leave a digital footprint suggesting their intention to buy. But often they’re searching for businesses while they’re on the road, or standing in line at a store about to buy. The time between expressing purchase intent and actually buying is a micro-moment. But it’s something advertisers can target when they use automation technology.
Bidding automation technology, for example, can make micro-changes to a bid strategy throughout the day in real-time based on the latest data insights. Instead of waiting on data teams and data scientists to make targeted changes, automation technology can ensure all relevant data is used in a timely manner to optimize campaigns.
There are lots of ways automation technology can help insights-driven businesses succeed today. Using the right data management tools in combination with internal business processes is essential to maximize the value of this strategy.
The Bottom Line
Today, there’s a wealth of relevant consumer data that businesses can use to optimize their marketing and sales strategies. The volume of relevant data is so great that customer data management technology is an essential requirement for success. But that’s far from the only thing enterprise-level businesses need to build an effective and efficient data strategy.
Insights-driven businesses choose the right technologies for their goals as well as develop internal processes to maximize their value. This includes creating an organizational culture that regularly utilizes data insights to drive change.
Explore how the QuanticMind platform could help you integrate your data and drive insights for revenue growth. Request a Free consultation now!