Financial institutions can hire all the outside experts and consultants they can afford, but the truth is unless senior management can get the entire organization thinking, talking and walking the path of data analytics, the effort will have limited results.
The challenge is to make sure everyone views customer data as a valued asset, and helping them understand the benefits of data-driven decision making. Once a coherent, integrated process for managing data and the analytics is developed, management’s role in maintaining and keeping the strategy healthy, growing and productive for the long haul is critical.
The sales of analytics software is expected to increase to approximately $17 billion this year. This means there will be an onslaught of new data analytics initiatives and existing data platforms will be expanding. As many financial institutions have found out (some too late) there are many moving parts to designing and building an effective data analytics platform. Here are eight critical concepts to keep in mind as you move from designing your strategy to making data part of your organization’s DNA.
1. The Customer Is The Center of The Universe
At the end of the day, any data analytics initiative should be about serving the customer — better, faster and cheaper. Analytics strategy has to be focused on identifying your best customers, what factors build loyalty, increase their share of wallet and grow household profitability.
A good place to start is with the segmentation of your customer base. Segmentation pays off throughout the customer life cycle — starting with acquisition and onboarding. When matching customer segments to select product/service offerings, you can never assume you know more about what the customer wants than the customer. That’s why you must focus on customer behaviors — what their actions (e.g., online searches, customer service interactions and transactions) are saying, not what we think they want or need. The goal is to match the right customer with the right product. Just remember: customer needs and preferences can change quickly, so it’s important to continually monitor these changes and update your database accordingly.
One way to ensure you have a robust and useful customer data set is to create ways that encourage customers to share their data. Most customers and even prospects will freely share their information if they receive something of value in return. Even though this approach is effective, few financial institutions attempt it with any energy or creativity
Many banks and credit unions have integrated Personal Financial Management (PFM) into their online and mobile solutions. These applications help customers manage their finances/budget within parameters they define. Users enjoy budget tracking and the attainment of personal financial goals through this disciplined approach. But these PFM applications are also a great place for financial marketers to gain a better understanding of people’s priorities and preferences — veritable goldmines of information.
2. Support From Senior Management Is Critical
Any data analytics strategy without broad C-level support will almost always end up stuck in the mud. “Support” means both financial/budget support and interal office politics — managing turf wars and facilitating interdepartmental cooperation. It’s critical that project leaders/advocates ensure managers at all levels and in all departments fully understand the payoff, because implementing a data analytics strategy is a process that involves both scale and effort.
If senior management buy-in doesn’t happen, little will become of the effort… if it ever gets off the ground at all. If your bank has already initiated a data analytics strategy, senior managers will want to see the impact and results of their investment. So you have to think of your lobbying efforts as a form of internal PR. Don’t let the effort die before it’s had the chance to be successful.
There will be conversations surrounding the ownership of data and who’s responsible for deriving value from the analytics process which can fundamentally redefine some roles in your organization. As data gets collected and centralized from many different areas of the organization, it exposes each department to increased scrutiny, which may make some managers uncomfortable. Questions may be raised: “Why aren’t you collecting data point [X]?” Not being able to control the flow of information, how it is evaluated, how it is distributed and how it is ultimately used will change relationships between senior managers and their various departments. This will leave some feeling apprehensive, and their anxiety is something that those spearheading a major data initiative will need to anticipate and manage.
3. Data Analytics Strategies Must Match The Growth Strategy
Most banks and credit unions acknowledge the necessity for investing in the development of data analytics functions, but many are reluctant to commit the budget and personnel resources to pull it off. Many times this is simply because senior managers are not sure how these new analytic tools will support the organization’s future growth and revenue goals.
As retail financial institutions continue to wrestle with a narrow operating margins, they must run leaner and more efficiently. CMOs are tasked with demonstrating how marketing generates revenue and contributes to the organization’s growth and revenue goals. The good news is that a robust data analytics initiative helps achieve greater clarity and insight into what’s going on and why.
Data analytics strategies can more closely align with the bank’s overall goals by better understanding some of the following:
- The bank’s mission statement. This document can provide the highest level of success criteria and the primary reason the bank is in business. Key phrases in the mission statement point to major goals, which lead to specific business objectives.
- The marketing/sales goals of the organization. Are there specific customer segments, geographic areas, and market share or brand valuation goals that the bank is focused on accomplishing?
- Marketing/sales mechanics.These measures relate to very specific subsets of tasks in the marketing process. These can include elements like media characteristics (particularly social media).
- Core competencies of the company. When evaluating a potential marketing opportunity it is important to factor into the decision whether the company has the expertise and/or resources to accomplish the opportunities being evaluated.
4. Training Is Key
The emergence of big data and the complexities of unstructured data require more senior executives in your organization to understand more of the mechanics than ever before.
Training and education should be provided for these decision-makers to in order to allow them to understand how to best gain insights from the information they have in front of them. This training should also give decision-makers the ability to adequately evaluate new insights and to integrate them into the bank’s core DNA.
General meetings about marketing and data analytics do not qualify as formal training on how to maximize the value of the institution’s data. Training specific and unique to each position is critical for the managers who must apply and evaluate these new data-driven insights on a daily basis, as well as senior-level managers who must figure out how to allocate and manage the resources required to optimize it.
There are some excellent data analytics overview courses available online, like Coursera’s Executive Data Science Specialization Certification, which is comprised of four intensive courses. This online program is designed to give marketing executives the knowledge and skill to assemble and lead marketing analytics teams within the enterprise. The idea is for management to be comfortable in conversing with the data scientists on staff and to be able to get the most from data analytics resources. It’s important for management to learn to speak geek.
5. Functional Integration of Strategy Is Key
A Forbes report identified several critical trends when it comes to marketing analytics strategy. The report noted that data-driven marketing must be an enterprise-wide effort — one that requires data, expertise and innovative thinking from many parts of the enterprise. You must ensure that the data strategy includes stakeholders from all functional areas of the bank. Stakeholder representation on the strategy and implementation teams is critical. To get organization-wide buy-in, everybody should be on board and have a contributing role in developing the data strategy.
Many financial institutions are unwilling to share data and results across functional areas, but making data analytics accessible across the wider organization will be a critical factor in making data part of your organization’s DNA. A strong and democratic data governance approach is one way to ensure that data and the resulting insights are at least shared by the most important internal constituencies — marketing, retail, sales, customer service, operations, human resources and finance.
6. Drive for Results, But Set Expectations at Reasonable Levels
While the potential impact of data analytics is significant, the path there is always clear or straight, so don’t create unrealistic expectations with your Board or senior management team. Success will be driven by many factors both analytical and organizational, and it will likely take a fair amount of time to mesh it all into an effective, well-oiled system. Often in the effort to get management approval for a data analytics project, there can be too much exuberance in the air. But the old adage, “under-promise and over-deliver” fits well here.
Change never comes as fast as we would like. It’s human nature many people and organizations are inherently resistant to change. When you are plotting your implementation timeline, do not underestimate how long it will take people in your organization to adapt to this new data-driven paradigm.
For data to become part of the bank’s DNA a significant shift in the institution’s culture will usually be required. This shift involves viewing (and using) customer data as an asset in decision-making, and it typically occurs more as a methodical evolution than an overnight earthquake. As various constituencies within the bank begin using insights to drive decision making and improving outcomes; the core processes driving the bank begin to evolve.
7. Balance Analytics and The Voice-of-the-Customer (VoC)
There is a danger in becoming so analytics-focused that you forget to listen to what customers are saying and how they are feeling (e.g., via social media). In simple terms, don’t let quantitative data squelch qualitative perspectives.
VoC listening initiatives can take many forms — for instance, qualitative research focused on the identification of service preferences and/or their financial goals. VoC studies typically consist of both qualitative and quantitative research steps, and are commonly used at the start of a new product/service development. This information is critical to better understanding factors that drive customer satisfaction, increasing each customer’s share of wallet and longevity with your institution.
To focus more on the VoC, many banks and credit unions are aligning themselves around the customer journey. In effect, they organize all customer facing departments around the customer experience. Because a strategy built purely around data collection and analytics can lead financial marketers to focus on the wrong factors that drive customer satisfaction. To develop a fact-based, data-driven culture it is critical to understand the customer experience and to organize your strategy, marketing functionality and analytics to match consumers’ preferences, behaviors and expecations.
A focus on the customer journey can help your analytics team discern between data noise and relevant data. Today with so much transaction, demographic and behavioral data available to financial institutions, it becomes increasingly necessary to better understand the difference between relevant data and noise. A data strategy has to relentlessly stay focused on what is important and impactful to the overall business strategy and model that drives growth and revenue.
8. Start Small
Banks and credit unions just starting out will want to develop a data analytics strategy that is big in its long term potential, but one that provides interim milestones based on the reality of available resources. Trying to do it all and be all things to all people too soon will leave many stakeholders with the sting of disappointment and a program in disarray. It is important from an internal and cultural point of view to develop pilot projects that deliver small but impactful wins — early and consistently.
For many smaller financial institutions, it can be difficult to dive right into the right analytics solution mainly because they don’t know what is possible. Many find success in starting small and getting their hands-dirty with data by creating a small database using a subset of their customer data.
Hands-on experience helps the bank’s leadership team to begin learning what customer data can do, the resources required, the organizational implications and to establish a realistic benchmark ROI. Having a better understanding of the impact of all these elements contributes to aligning your data analytics goals the overall business goals and resources of your institution.