Data as an Asset Introduction: According to the European Commission, the value of personalised data – just one class of data – will be one trillion euros, almost 8% of the EU’s GDP. Data can be extremely valuable if the right conditions are met. For the data to have value it must satisfy some basic premises: It must be under the organisation’s control – the organisation must have rights to use the data in a way which is compliant with laws, whilst also protecting the data and restricting access to it by others.&nbsp; It must promise probable future economic benefits – use cases capable of driving incremental future cash flow which must be identifiable and definable – a data asset may be made up of specific files or specific tables or records within a database. Assuming an organisation invests the time to create an inventory of its data, the value of that data lies in its ability to allow an organisation to generate future economic benefit (‘data monetisation’). Challenges to Making Data an Asset: Many understand that data is the new gold—yet despite massive investments, many companies struggle to mine and refine it successfully. There are many reasons, including data fluency, complex and siloed system architectures, access controls and policies, cultural issues, and interoperability issues across the business. Only 17 percent of organisations say they are very or extremely effective at maximising the value from the data they hold.—Harvey Nash/KPMG CIO How to Make Data an Asset: To capitalise on data as an asset, organisations will need to become more data driven—and data fluent. To reap data’s full benefits, it’s imperative to invest in programs to upscale the data and analytics skills of staff across the business and set baseline expectations for data literacy and usage throughout the organisation.&nbsp; To supply the information the business uses to inform decision making, data must first be made available. Typically, data curation is carried out by data engineers and data architects, who create and manipulate &amp; join an array of data sets, tables, and systems. Thereafter the data is visualised to enable self-serve decision making. The Below 4 Steps will Support Data Asset Ambitions: 1. Create a data value proposition grounded in business imperatives Data enables business strategy - it’s not a business strategy itself. Before an organisation moves forward with data initiatives, there needs to be a well articulated value proposition that holistically describes the challenges, problems, or hypotheses that data will help address.&nbsp; 2. Clarify functional accountabilities built into the operating model Successfully using data as a valuable business asset requires clear accountabilities and spans of control for key technology leaders—and buy-in from those leaders. 3. Start the organisation’s journey toward data literacy Data literacy is critical for creating a strong data driven culture and enabling the business to make the best use of data. 4. Manage the cultural shift Digital transformation and embracing a data-driven way of doing business represents a fundamental cultural shift for most organisations and that shift needs to be managed effectively. New ways of working, tooling, and governance will be introduced at every layer of the organisation. Stakeholders across the enterprise will need to understand how data will change the business, from decision making to delivery and beyond. Types of Value Creation from Data: The income approach: measures the incremental cash flows which the use cases are expected to generate.&nbsp;The cost approach: data which is purchasable from a buyer. Summary: Organisations are under increasing pressure to invest in data assets, either organically or through acquisitions. Assessing those investments robustly and making the right strategic decisions requires an understanding of data valuation methods and value drivers. As a key feature to unlock business strategy and subsequent value, especially pertinent in the context of Artificial Intelligence, expect more companies to place increasing emphasis on data. References: <ul><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2019/10/data-as-an-asset.pdf">https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2019/10/data-as-an-asset.pdf</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.pwc.co.uk/data-analytics/documents/putting-value-on-data.pdf">https://www.pwc.co.uk/data-analytics/documents/putting-value-on-data.pdf</a></li><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.oecd-ilibrary.org/docserver/b840fb01-en.pdf?expires=1721045378&amp;id=id&amp;accname=guest&amp;checksum=27A3CA3AB75189135FDDD414F9B645E4">https://www.oecd-ilibrary.org/docserver/b840fb01-en.pdf?expires=1721045378&amp;id=id&amp;accname=guest&amp;checksum=27A3CA3AB75189135FDDD414F9B645E4</a>&nbsp;</li><li><a target="_blank" rel="noopener noreferrer nofollow" href="https://anmut.co.uk/why-youshould-be-treating-your-data-as-an-asset/">https://anmut.co.uk/why-youshould-be-treating-your-data-as-an-asset/</a>&nbsp;</li></ul>

Using Data as an Asset

PurposeThe below highlights Day1Data’s approach to Marketing Mix Models (MMM), including tips &amp; tricks for its implementation. What is a Marketing Mix Model?Marketing Mix Models are a set of models which are used to measure true marketing incrementality. They were originated in the 90s by XYZ as a solution to measure marketing effectiveness &amp; optimise budgets towards certain goals.&nbsp; The models quantify the impact of offline media (i.e. OOH advertising), which can help Marketing departments put an end to the ‘brand’ vs ‘performance’ debate. The models also capture seasonality and macroeconomic events (like unemployment rates, GDP etc.) making it a really efficient method of capturing the natural demand of your product or service. The MMM can be tuned to support the commercial mix, provide actionable insights around pricing &amp; promotions strategy. However, most importantly, it’s a solution that doesn’t rely on cookies. Google reports that by the end of 2025 there will be no more advertising cookies due to data protection advancements. MMM represents a future-proof solution to marketing measurement.&nbsp; Is MMM right for you?Firstly, it’s important to caveat that MMM cannot &amp; should not be implemented by every company.&nbsp;To create an accurate MMM, a company will typically need 3 years worth of historical data, including varied spend levels, business strategies &amp; overall performance. Additionally a variety of paid marketing channels is also crucial to help establish sub-channel effectiveness. These variables help inform the model. In our experience, a company will need to be spending over $100,000+ per annum on marketing to help the model get an accurate read &amp; to justify the roll out of the models due to the costs associated on building it, specifically: <ul><li>Creating an econometrics team</li><li>Spend on a vendor / consultancy</li><li>The data storage &amp; querying costs</li></ul> The above should be considered investments in ROI calculation: you’ll need the value to justify the cost. This is the same for both D2C &amp; B2B companies, the thing that will change is the model KPI(s) &amp; the data inputs. When applied in the correct conditions and the business is primed to operationalise the insights, the MMM can have an overwhelmingly positive impact. Moreover, MMM is not a pure attribution solution, such as multi-touch attribution. It is about incrementality. This means it’s an actionable methodology to support measurement, scenario planning, forecasting and budget optimisation. It should be used in conjunction with continuous experimentation to help optimise the MMM outputs.&nbsp; Despite this, the methodology isn’t without its risks. Determining model accuracy is really difficult in the absence of testing the model outputs. The model metrics can lie. It’s possible to make models statistically accurate (in terms of the MAPE or R2), but these may not be a true reflection of your business performance. That’s why we’d recommend only rolling MMM out when used in conjunction with incrementality testing to validate the output. Additionally, as referenced earlier - traditionally this solution is not the cheapest option.&nbsp; With this in mind, here’s our advice on when to use MMM: <ol><li>Use MMM only if the internal company circumstances match what we laid out within the article introduction<ol><li>3 years of historical data</li><li>Large spend levels</li><li>Varied media mix</li></ol></li><li>If your company can determine Marketing diminishing returns via other means such as last-click attribution &amp; testing, then MMM is not a necessity for you</li><li>Use MMM in conjunction with incrementality testing (which is the ground truth). The testing output can be included as a model feature in order to improve the accuracy</li></ol> What are the options for implementing an MMM?If you’re looking to kick off your MMM journey, there’s a variety of options you can take: <ul><li>You can build the models in-house leveraging open source versions of MMM foundational code. Robyn from Meta &amp; Meridian from Google are great options<ul><li>Pro - all IP stays in-house</li><li>Con - MMM expertise is normally required to build something accurate &amp; meaningful</li></ul></li></ul> <ul><li>There’s subscription based SaaS tools which are available, with extremely low barriers to entry, for example Fospha or Lifesight<ul><li>Pro - very fast modelling turnaround time &amp; intuitive dashboards to help bring insights to life</li><li>Con - dependency on a 3rd party tool, black box solution, high cost, accuracy,&nbsp; internal teams need to be involved in data and infrastructure provisioning</li></ul></li></ul> <ul><li>Varieties of consultancy based approaches:<ul><li>Larger consultancies have proprietary MMM solutions (which are typically black box), however<ul><li>Pro - they bring years of experience of MMM &amp; industry benchmarks which are useful</li><li>Con - the solutions are black box, the price is typically extremely high, internal teams need to be involved in requirements &amp; data provision</li></ul></li></ul></li></ul> <ul><li>Smaller consultancies who help build the models for you<ul><li>Pro - they’re an extension of the internal team, IP stays within the company, lower prices, accuracy</li><li>Con - internal teams need to be involved in requirements &amp; data provision</li></ul></li></ul> The Day1Data approach is in line with the small consultancy way of working. We’d build bespoke models for the company, ensuring IP stays within the firm &amp; specific company nuances are built into the model. This is our preferred approach as it is extremely transparent. We believe transparency is crucial. Marketing spend represents over 40% of an average companies’ expenditure - the stakes have never been higher to de-risk harmful growth decisions. More transparency into the model, leads to more trust, to help steer companies to make data-driven decisions &amp; operationalise the outputs.&nbsp;

Day1Data is your external, end to end marketing analytics team, delivering you value without the need for permanent hires.