5 Ways You’re Not Using Data in Your Marketing Strategy

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5 Ways Youre Not Using Data in Your Marketing
Strategy
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(No Transcript)
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• Despite the fact that most experts in the
  technology sector have been continually advising
  on the need for widespread application of
  analytics dashboards and data-driven KPIs, most
  senior marketing teams are still utilizing data
  primarily for backward-looking analysis to
  measure performance rather than building
  analytics dashboards that drive future
  initiatives and planning.
• Even with the advent of AI empowered predictive
  analytics, a recent study from McKinsey found
  that only 17 of businesses report they are
  actively using data to build future strategic
  planning and better CX models. What makes this an
  even more frustrating predicament is that the
  majority of brands are either underutilizing data
  that they already have at their fingertips are
  ignoring the absolute necessity to have a
  Customer Data Platform that provides them with a
  future-ready deployment strategy.

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• Theres often untapped value to be found in
  re-engineering data points or capturing new types
  of data to which your current CDP might not have,
  such as Zero Party Data (data supplied by the
  customer themselves to the brand.) What you can
  learn from a deeper dive into the data pool is a
  better understanding of your target audience and
  how to best optimize a CX campaign that speaks
  more effectively to your customers. A stronger
  commitment to data can make you more effective in
  converting new customers but can also result in
  customers with higher brand engagement and higher
  lifetime value.

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Here are 5 areas where you can make that
commitment count

• 1. Know Your Target Audience
• With broad audiences, its hard to deliver
  campaigns that are relevant to everyone.
  Therefore, one of the first steps to achieve
  better results in campaigns is to better know
  your target customer and segment the audience in
  a meaningful and actionable way, so youre able
  to serve the direct needs of each small audience
  cluster, and potentially, each individual
  customer unit.

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• An effective way to perform audience segmentation
  and targeting is through a combination of
  marketing methods and data science. This
  framework allows you to have a comprehensive
  understanding of different consumer profiles with
  different behaviors and needs, so as to convey
  the right message to the right audience and
  ensure that products and services are clearly
  communicated to meet their needs and help them
  achieve satisfaction with your brand.
• Data science enables the analysis of large
  volumes of data, with the use of sophisticated
  statistical techniques, which allow for finding
  patterns among consumers. Thus, demographic
  characteristics, geographic information, product
  use, and behavioral characteristics, for example,
  can be used to analyze and segment consumers.

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• Design thinking processes, on the other hand,
  allow us to analyze consumers in depth and, thus,
  identify the most relevant factors to segment
  them according to their needs as well as to
  create personas. When both are combined, it is
  possible to identify the patterns of similarity
  and dissimilarity, considering the most important
  factors, in addition to understanding the
  relevant characteristics that differentiate and
  describe them, which supports the creation of
  campaigns that resonate better with them.

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• This design-driven data science framework is
  normally based on in-depth qualitative interviews
  with consumers to understand customer profiles
  and needs more deeply on the analysis of large
  volumes of customer data for generating insights
  about customers behaviors, preferences and
  profiles on advanced analytics techniques and
  machine learning (ML) to cluster customers and
  perform statistical analysis and on the use of
  frameworks such as jobs to be done to capture
  consumers needs.
• This process is iterative and provides a means
  for testing hypotheses generated on the
  qualitative interview. Also, as some of the
  consumer insights generated from data analysis
  are based on correlations, which does not imply
  causation, data insights can also suggest some
  points to be explored more deeply on the
  qualitative interviews.

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2. Optimizing Acquisition Cost By Predicting
Lifetime Customer Value

• Marketers are always under a strict budget. Its
  important to optimize spending to assess maximum
  ROI from their allocated budgets. Data analysis
  and machine learning can be great tools to
  improve customer acquisition processes and reduce
  its costs. Data can support the estimation of the
  customer acquisition cost (CAC) as well as the
  customer lifetime value (CLV),starting with a new
  customers first purchase or contract and ending
  with the moment of churn.
• By calculating the CLV, companies can evaluate
  how much to invest in a customer and evaluate the
  different strategies and levels of investment
  that are needed in order to acquire new customers
  with higher value.

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• There are several different ways to calculate
  CLV, depending on whether the business operates
  contractually (e.g., Netflix, credit cards, SaaS
  business)or in a non-contractual setting (e.g.,
  online retail, grocery stores) as well as if the
  transactions are discrete (e.g., monthly/yearly)
  or continuous.A more complete CLV methodology
  uses predictive analytic models and requires
  advanced statistical knowledge to perform a more
  accurate estimation of the CLV of each customer,
  providing a more robust and dynamic metric. By
  segmenting customers with this metric, its
  possible to understand demographic and behavioral
  traits of the most valuable customers and even
  train a machine learning model to predict the CLV
  segment of new leads, which allows companies to
  optimize customer acquisition budget. Also, its
  possible to perform customer look-alike targeting
  to find new leads similar to the higher value
  ones.

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• Sounds pretty nifty, right? It is, and its the
  path that savvy marketers must take if theyre
  going to stay competitive. To do it right,
  however, you need to have the correct tools in
  place, like Group FiOs Intelligent Customer Data
  Platform(CDP) , and you must also have the
  capability to develop a comprehensive strategy
  behind it. Know more please visit our blog page
  https//www.groupfio.com/5-ways-youre-not-using-da
  ta-in-your-marketing-strategy/