In the past decade the amount of data that companies have captured has exploded and the tools that companies are using are continuously generating more of it. Data related businesses delivering possibilities to leverage external data have also grown tremendously and the new leap in making data actionable is about connecting internal and external data effectively and crafting intelligence from it with algorithms.
The approach is best to describe with the Double Diamond Framework (Figure below). It was popularized by the design thinking community since the mid 2000s (Design Council, 2022), it builds on the works of Guilford (1956), who is generally attributed with originally making the distinction between divergent and convergent thinking. Innovation process concists of a sequence of steps where innovators first explore a wide range of problems and opportunities to then decide on adequate solutions. (Source: Augmenting human innovation teams with AI intelligence: Exploring transformer-based language models)
This same process has been used for the purpose of making creative strategy by eg. Ogilvy Group. We are using it to make a strategic discovery for strategy design and for the deployment and management of change. Like this:
Stage 1: Aggregating company's own data and relevant external data, which is about enablement for the analysis. The idea is to use unusually large bottom-up and outside-in dataset in laying the foundation, which will extend the double diamond AI enabled insight and priority generation in next stages.
Stage 2: Using algorithms to make sense of the data of tens of millions of rows of raw data. Discovering where the money comes from, what offering and segment related dynamics, insights and priorities can we recognize, Discovering the drivers of readiness to adopt new offerings (market segmenting by Addressable, Obtainable and Active markets), how customers map in to current and potential value segments etc. In Stage 2 the primary goal is to discover what matters (drivers of Risks and Opportunities) and where's the money. We need to get behind KPI's and reveal dynamics and phenomena that drive change and results. Once recognized, we can measure and manage them. More about Discovery phase as a use case here
Stage 3: Strategy is a domain of human and especially management consideration and decision making. The usual use cases would be eg. creation of strategic segmentation, making portfolio analysis, creation of strategy and implementation planning or planning and deployment of OKR. The third stage is about defining direction, vision, brand and narrative. It gives the foundation for Strategic development programs and deployment planning.
Stage 4: Preparing for the deployment, defining team objectives, teams and their key results. Each team has access to data that is relevant for their Objectives and they have tools to form their goals in key results as well as capability to analyze what are the underlying challenges and dynamics that they should concentrate on. The idea is that the objectives of the programs are firmly built on bottom-up data, which enable us to give tools and answers for planning. One area of development could be eg. ABM and performance marketing development. If we require strong growth, we need to give tools to meet them. 180 OPS has been built to give those answers (see about us).
Stage 5: Learning and Doing. Transparency and shared learning is imperative for continuous improvement and cumulative development of competitive advantage. The markets and customers change continuously and 180 OPS monitors those changes and gives feedback for eg. changes in customers willingness to buy or relationship or offering related risks. Adaptability to changing environment is a must and early signals about changes are imperative. That is why outside-in data has a very important role. Getting advice at individual customer level support sales very effectively. Creating unity between offering, marketing, sales and customer service bring the entire customer interface together. Offerings, customer segments and activities interconnect and transparency and feedback inspire, empower and energize the organization. The goal is to drive cultural unity and purpose, winning as a team.
The new discoveries and magic are available from combinations:
Outside-in logic is about leveraging customer and market need status data. Many decisions related to customer needs and timing are caused by changes in customer’s situation. The change in turnover or profitability, M&A activities, what technologies are they using, where and how do they operate, new CEO or strategy, etc. This data is available at individual company level and at macroeconomic level. The changes in business environment have impact on customers’ choices and timing. Natural and most obvious data sources are eg. Changes in debt interest rates, gross domestic product, employment or changes in market confidence. Our role is to provide you tools to analyze how the changes impact customers and how you can leverage them.
Great example of external data is credit rating which has a long history. Credit rating was created for risk management to make choices about pricing and defining which companies are safe to serve and which shouldn’t be served at all. However, when we analyze credit rating changes with time series analysis it is quite possible to learn, that changes in credit rating represent positive demand signals for some offerings and negative for others. Change in credit rating can be a relevant signal that reveals something about the company’s changing needs. This is what we are ultimately learning about, discovering underlying signals that have a story to tell and can advise us about how to target and time activities for higher impact.
Offerings can also represent very different life cycle stages. This challenge can be partly answered with own data: Which offerings are growing, stabile or declining. If some offering is declining, there are no new offers and existing customers are cutting their investments and defecting, potential is not applicable. The external data can give insights about how others are doing in this specific offering range.
In B2C markets motive and attitude based segmentations have been studied and have sometimes been quite effective in defining who and why to target. The difficulty in B2C markets is about connecting people and data. There is no data on individual customers’ motives available at least internationally and legislation is regulating consumer data heavily. In case of businesses this analysis is much more practical because the motives have their foundation in data that can be analyzed and quantified. This makes motive based segmenting more applicable in B2B than it can be in B2C.
External data sources can be divided in groups by their speed. Financial data is mostly more than one year old, credit rating can change multiple times in a year, some other variables can be almost real-time. The new area to dive in to is about curation of external data that delivers most value when combined with internal. Because this data is about companies and markets it is not subject to GDPR and other privacy laws and is both richer and easier to manage in legal perspective than personal data would be in consumer markets.
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Learn more:
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Boost your productivity with RevOps
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How to segment markets by urgency: Addressable, Obtainable and Active Markets