B2B Marketing Evaluation Lacks Trustworthiness and is About to Deteriorate Further
B2B Marketing Evaluation Lacks Trustworthiness and is About to Deteriorate Further
Prepare yourself for some brutal honesty. Reliance on marketing measurement evaluations is currently weak, and if left unchecked, it's projected to plummet by 20% more.
The discomfort experienced by businesses in dealing with B2B marketing measurement dilemmas is valid. According to Forrester's Marketing Survey of 2024, 64% of B2B marketing bosses express doubt in their organization's measurement as a decision-making tool. This is disheartening because marketing relies heavily on data, facts, and insights, yet numerous leaders don't trust the measurements of their companies when circumstances demand it the most.
Lacking reliable measurements hinders marketing's capacity to execute effectively. Optimization of marketing initiatives calls for data to direct alterations. Regrettably, this can't happen when the metrics used to gauge performance are untrusted. Moreover, without dependable measurements to delineate marketing's influence, securing the resources and funds necessary to produce business-altering effects becomes an uphill struggle.
Nothing new is being presented here, nor are the leading causes of marketing measurement's current state (data quality, technology loopholes, and skills of measurement producers and users) obscure. All are concerns B2B organizations are constantly working on improving; however, unfortunately, many B2B marketers may fall behind in these struggles throughout the approaching year.
Why is Measurement about to become more complicated?
We expect marketing measurements to become more challenging due to these intensifying market pressures:
- Compounding buying complexity confuses. B2B sellers inform us that purchasing cycles have yet lengthened, and buyers express the large number of individuals involved in making choices. Persistent lag issues in recognizing business impact grow harder with extended selling cycles. More people communicating with sales and marketing more frequently puts additional stress on measurement systems already straining to capture and decipher behavior. As long as vendor organizations continue to improperly calibrate their business systems and procedures, they will only be able to detect a small percentage of total buying interactions and uphold a lengthier wait to comprehend results.
- Technology overabundance leads to fractured data. The multitude of digital marketing technologies comprising the go-to-market technology stack leads to disparate data sources, leading to fragmented data, now a major challenge in data analytics. Synthesizing a cohesive picture of buyer behavior across these technologies is taxing the resources and competence of data analytics teams – a challenge that shows no signs of abating.
- Exaggerated AI expectations drive an expectation gap. AI possesses the potential to positively influence numerous areas within B2B. This belief propagates neat expectations that B2B measurement can be improved by using AI to interpret widespread volumes of data at speeds humans can never equal. However, the distance between this conception and the present state of B2B marketing measurement should be quantified in years not months. B2B planning, processes, and data are not yet prepared to capitalize on AI's potential. Users of all types will struggle for years on end to comprehend and have faith in AI-driven views of performance. Expect a prolonged period of experimentation, errors, and resets before B2B marketing analytics teams approach cracking this code.
- Measurement can't keep pace with the rekindled focus on reputation investment. B2B marketing spend has historically centered on capturing and boosting demand, and in turn, so has the emphasis on marketing measurement. However, there's growing acknowledgment that demand approaches are not sufficient, and selling organizations must do more to shape buyers before they embark on active purchasing cycles. Reputation expenditure now represents approximately a quarter of marketing budgets, yet we're not observing a corresponding rise in investment in the metrics by which this area is measured. Marketing analytics teams currently fail in the abilities and competencies to measure this previously neglected domain.
What's the solution?
These market pressures are formidable, and generating any meaningful impact on them is beyond the reach of your data team. What sets successful companies apart is their response. In the face of these forces, here are some actions you can undertake to strengthen your business's belief in marketing measurement:
- Optimize processes for buying complexity. Invest the effort to align buyers to opportunity records, and work to record not only self-guided interactions but personal ones as well.
- Eliminate redundant B2B technologies. Slim down the capabilities found in specialized solutions in favor of the extensive solutions provided by platform providers. A more consolidated set of technologies will carry less overhead when it comes to data integration and synthesis.
- Set clear reputation targets. Allocate time and resources to creating comprehensive strategies for measuring reputation. In the interim, work with stakeholders to clarify reputation goals and select a few available indicators that can display progress.
- Match AI initiatives with insight enactment. Marketing professionals are justified in their enthusiasm for AI. Simultaneously, the necessity to empower them to work more efficiently with the existing analytics is clear. Marketing analytics teams must reallocate more of their time towards aiding their stakeholders to yield better results with their available resources. This will better prepare them for the potential that AI will eventually unlock.
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This post was penned by VP, Principal Analyst Ross Graber and originally appeared here.
To mitigate the challenges facing B2B marketing measurement, innovative approaches are necessary. Embracing enterprise solutions in tech, such as consolidating B2B technologies and optimizing data processes, can help improve data integration and synthesis. Furthermore, enterprising companies should focus on setting clear reputation targets and utilizing AI to enhance insights and efficiency, which will enhance trust in marketing measurements.