The interest in AI, data analytics and data initiatives has been drastically rising— especially more during the current Covid-19 situation. Despite their best efforts, asset managers are not experiencing the growth and development they were expecting from their digital initiatives. Asset managers have made concentrated efforts to adopt analytics into their marketing goals and objectives but still can’t seem to catch-up. What are they missing?
Increasingly, asset managers are starting to realize the importance of leveraging data and analytics, especially within the marketing side of business. The conversation we have with heads of marketing and heads of digital indicate that they are aware of terms such as “data lake”, “advanced analytics”, “AI”, etc. but often struggle with opinions and claims.
The surge of interest is influenced by the following factors:
- Increased need to establish themselves as thought leaders in the technical space, and the desire to create differentiation amongst customers with their content and initiatives.
- Shift in focus towards digital transformation and personalized marketing.
- Strong outlook towards AUM growth and sales enablement, while trying to keep cost at a minimum.
Stages of analytics maturity
By gathering information from some of the top 15 asset managers in the US, we mapped what stages they would be in the analytics maturity graph. Each stage of the graph corresponds to the level of utilization of data, analytics and visualization capabilities within their marketing groups.
Stage one: Ingesting
The ingesting stage is when an organization is getting introduced to all of the data analytics capabilities. During this stage, organizations are still exploring what vendors they should engage with and what technology they should invest in for their teams. There is no long-term goal or plan in place for this stage, so most of the efforts made are ad-hoc. Also, without any deep knowledge on it, organizations in this stage tend to utilize data and analytics as they see fit.
Stage two: Orienting
During this stage, organizations start to plan and prioritize initiatives based on business value to different LOBs. They are still at a nascent stage and have to deal with a lot of fragmented data reporting. However with the increased level of situation awareness, organizations start to build baseline measurement frameworks, perform diagnostic assessments, and also realize the importance of having a centralized data repository (e.g. build capabilities like big data warehouses, data lakes and etc.).
Stage three: Advising
At this stage, organizations have a clear data and analytics strategy in place. They use advanced analytics and model-based decisioning to quantify the impact of different initiatives. After graduating from diagnostic studies, they are also now able to utilize the predictive and cognitive power of analytics for their planning exercises.
Stage four: Deciding
This stage is characterized by the tight integration and governance on the usage of data and analytics across the organization. There is a heavy focus on automation, organization-wide adoption through insights and storytelling, and on building rapid response capabilities with the inclusion of AI and machine learning (ML).
Stage five: Learning
At this stage, an organization would have data and analytics deeply embedded into their marketing efforts. Organizations are now ready to adapt to dynamic conditions with better planning and optimize their budgets accordingly. Real time measurement, simulation tools and interactive platforms now take the center stage.
The current state of asset management
After analyzing the analytics maturity of the top 15 asset managers in the US, we have found that the majority of them are still at the ingesting, orienting and advising stage. There can be many factors that can prevent asset managers from reaching analytics maturity, we have found that the following points are some of the main reasons for this stunt in growth:
- Coping with increasing cost pressures—Having a robust data analytics plan does not come cheap. Organizations are unaware of the long-term benefits and growth that would come from investing into data analytics.
- Lack of dedicated data analytics team—Establishing an in-house multi-disciplinary team in a cost-intensive environment fails to achieve the required scale and impact
- Struggling to find the right partner—Finding the right implementation partner that can focus on the bigger picture, while also driving ground level changes at the same time, is difficult to find.
How can asset managers make progress?
Markers of success
While dealing with road bumps and the struggle to choose the right analytics implementation partner, asset managers can use the following markers of success to evaluate a right kind of engagement.
The first marker of success is prioritizing efforts based on business value. Asset managers should identify pilot projects that would best fit and maximize the value added to their organization. The selected projects should be ones that result in quick wins for their organization, with measurable business impact.
The second marker of success is a focus on ‘last mile’ adoption. This is a consultative approach that helps promote usability and adoption by business-end users. The key is to integrate data and analytics into existing workflows, so that it is easier for different teams to effortlessly adapt to changes. Promoting analytics usability can be done through something as simple as institutive visuals and digestible story boarding.
The third marker of success is a ‘team effort’ approach. Successful implementation of data and analytics capabilities requires a multi-disciplinary team. Rather than building it in-house, an asset manager can look for a partner that follows a flexible engagement model and has an innovation lab set-up to dynamically support varied skill requirements.
The fourth marker of success is a minimal viable product approach. One of the biggest mistakes that asset managers make when dipping their toes into the world of data analytics, is that they go head-first and try to do it all at once. Instead, they should follow an agile development and delivery approach. Through this continuous ‘Test and Learn’ process, asset managers can start with a few use cases that prove to be a success and then scale-up accordingly.
The fifth marker of success is having a self-sustaining engine. This helps asset managers remember to focus on the bigger picture, rather than being hyper-focused on tactical uses of data analytics. Look for partners that are aligned to your organizational goals, have a clear vision of the end-state, and know how to reach it with an ROI-driven mindset.
Once asset managers begin their analytics journey, their roadmap should be divided into three key stages, ranging from quick wins to he matured stage. This roadmap is very important as projects at each stage should align with the broader business objectives and the current data maturity of the organization.
Analytics RoadmapOne of our clients, a top 10 asset management firm focusing primarily on institutional business, wanted to establish the capability of data-driven decisioning across their marketing and sales organization. According to our analytics maturity model, the organization was at the ‘Orienting’ stage where they had invested heavily into a huge tech stack but had very fragmented use of data and analytics. They were working with multiple vendors but failed to establish strategic relevance and connect with the broader marketing charter.
Our team first helped the client create a strategic roadmap for data and analytics projects. The goal of this, was to help the client see their path of progression, and also focus on quick wins that can show immediate impact to the business. After planning and setting-up a team, the execution phase began with building a strong foundational layer of performance measurement across all of their marketing touchpoints. This included logical aggregation of data into a centralized repository. This resulted in a solid base for our teams to analyze any marketing or sales campaign, and then apply advanced modeling techniques to provide insights back to the client for planning the next one. Along the way, we also followed a customer-centric approach to assess the impact of marketing initiatives on customer journey and enhancing the customer experience quotient throughout all their digital platforms. The client was already on the path to digital transformation, and our combined efforts helped them get a wider reach and deeper customer engagement through their digital efforts.
Get a partner in it for the long haul
Planning where your organization wants to take the analytics journey is just the start, finding a data analytics partner who will be there from beginning to end and anywhere in between is just as important. Despite the promise and possibilities that come with data analytics, the success of it is dependent on a trusted partner who has deep knowledge and experience with data analytics. Find a partner who will take the time to understand your organization from the inside-out and will guide you in the direction that best suits your needs and capabilities.
- Data analytics is more than just a set of use cases, it enables meaningful growth and long-term growth for an organization.
- Asset managers need to take the time to assess and measure their data analytics maturity, or they will be left behind.