Digital Marketing Analytics for New Markets

This assignment delves into the application of digital marketing analytics in reaching new target markets. It highlights the significance of a data-driven approach, integrating various analytical tools to generate actionable insights. The focus is on leveraging these insights for effective campaign management, budget allocation, and ultimately driving conversions within the chosen new market.

Digital Marketing Analytics 1
DIGITAL MARKETING ANALYTICS
By (Student’s Name)
Professor’s Name
College
Course
Date

Digital Marketing Analytics 2
1. Information and a rationale
Introduction
There is no doubt that developing better analytical approaches and tools in the recent past
has provided business leaders substantial novel decision-making firepower. However, whereas
the advanced analytics offer the ability to boost growth alongside marketing return on
investment, firms appear nearly paralyzed by the offered alternatives. Consequently, business
leaders tends to depend on merely single planning alongside performance-management
approach. They swiftly discover that even the highly advanced single methodology has restrain
(Grönroos 2011).
The diverse tasks and audience which marketing dollars characteristically back and a
range of investment time horizons necessitate the need for increasingly sophisticated approach.
The best ways for leaders, therefore, to enhance effectiveness of marketing is the integration of
the marketing return on investment options into a way that exploits the best asset of each. This
leads to enormous benefits: an integrated analytics approach frees up about fifteen to twenty
percent of marketing expenditure. Globally, this equates to about $200 billion which can be
reinvested or drop straight to bottom line.
Anchor marketing analytics to strategy:
The overarching strategy of a firm must ground its analytical options choice. In the
strategy anchor absentia, it is discovered that a firm usually assign marketing dollars on the basis
of past budget or on what line of business or product fare generally well in the latest quarter.
Such approaches are able to devolve into “beauty contest” which reward coolest proposal or even
division which shouts the loudest instead of an areas which most require to grow or even defend
its present position.

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The best valuable approach measures proposals on the basis of respective strategic return,
payback period and economic value. The options’ evaluation based on such scores give a
consistent reflection for the contrast and comparison, and such measurement are able to be
merged with preconditions like baseline expenditure, prior commitments and threshold for some
media.
Another pre-requisite that shapes an effective marketing return on investment portfolio is
the comprehension of the organization consumers’ purchasing behavior. Such a behavior has
since radically changed in the previous 5 years that ancient ways of thinking regarding
consumers-like marketing “funnel”-overal do not apply. In instance that funnel approach
preferred the generation of as much brand awareness as feasible, the journey for decision of the
consumer acknowledges that process of buying remains more dynamic and that this behavior
remains subject to various different instances of influence.
Identifying the best analytical approach
For a firm to create the right marketing market, it needs to evaluate both cons and pros of
each of the available methods and tools to determine the best which supports its strategies. The
firm can choose from a range of existing choices:
Advanced analytics approaches like marketing-mix modeling (MMM). This utilizes big data in
determining spending effectiveness by channel. It statistically link marketing investments to
additional sales’ drivers and usually encompass external variables like seasonality and
competitor alongside promotional tasks to unearth both interaction effects (variations amongst
online, offline, and- social media activities, in most advanced models) and longitudinal effects
(fluctuations in segments and individual over a period).

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MMM is usable for both long-range strategic intentions and short-range tactical planning.
However, it has some shortcomings: it needs high-quality data on marketing expenditure and
sales tracing back to years; it can’t measure tasks which alter little over a period; and it can’t
measure long-run impacts of investing in any given single touchpoint; and further needs users
with adequately deep econometric knowledge to fathom the modes as well as a scenario-
planning technique to model implication of budgets of expenditure decisions.
Heuristics like reach, quality, cost (RCQ). This disaggregates every touchpoint into its
elements parts-number of target consumer reach, cost a unique touch, and quality of
engagement-utilizing both structured and data judgment. It is usually utilized where MMM is
never possible, like where there is restricted data; where the spending rate remains comparatively
fixed in the entire year; and with persistent, usually-on media whereby marginal investment
impacts remain difficult to isolate. The RCQ brings each touchpoint back to single unit of
measurement for easily comparison. It remains comparatively straightforward to perform,
usually with little more than an Excel Model. Practically, albeit, calibrating each touchpoint
value is quite challenging provided differences amongst the channel. It further lacks the
capability of accounting for network or interaction impacts and remains heavily reliant on
assumptions feeding it.
Emerging Approaches like attribution modelling: With the increasing movement of
advertisement dollar towards online, attribution has since increasingly becoming significant for
online media marketing and buying performance. Attribution modelling denotes the set rules and
algorithms governing how credit for conversion of traffic to ales is allocated to online
touchpoints like an e-mail campaign, social-networking feed, online ad, or even website. Such
credits assist marketers with the evaluation of comparative success of dissimilar online

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investment tasks in propelling sales (Cova and Dalli 2009). The most commonly utilized scoring
methods assume a basic rule-oriented approach like “last touch or click,” that allocates 100% of
credit to last touchpoint prior to conversion.
However, novel methods which utilized statistical modelling, regression methods
alongside sophisticated algorithms tying into-real-time bidding systems remain continuously
gaining traction for respective analytical rigor. Whereas such approaches remain a move up from
techniques attached to rules, they persistently characteristically rely on cookie data as the input
that restricts the data set richness and accordingly makes it hard to attribute accurately each
online touchpoints’ importance.
Integrating capabilities to produce insights:
Greatest return arise when a firm uses MROI tools in concert. Approach which is
integrated that pulls in direct-response data as well as insights, minimized biases inherent in a
single MROI technique and hence give business leaders flexibility to shift budget towards tasks
which generate greatest bang for buck.
3. Putting analytical approach at heart of organization:
It is never unfamiliar for teams to undertake analysis outsourcing or pushing to the wall
to the internal analytics cohort. Where results return, yet, such identical teams could be hesitant
to implement them due to lack of full understanding or numbers’ thrust. Thus, this problem is
solved by having marketers closely working with data scientist, digital analysts and marketing
researchers to probe assumptions, hypotheses formulations as well as fine-tuning the math. Firms
needs further to cultivate “translators” personnel who both comprehend the analytics as well as
speak business language (Castells 2015).

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Agility and speed remain equally imperative. Insights from customer-decision path and
marketing mix assignment need to inform tactical media mix. Real results need to be compared
with target numbers as they trickle in, with the budget and mix modified accordingly. Attribution
modeling is helpful, particularly with in-process campaign alterations, since digital expenditure
is modifiable on extremely short notice. The best-performing firms are able to reassign as much
as eighty percent of respective digital-marketing budget in the course of campaign (Blackwell,
Lauricella and Wartella 2014).
2. Target market
The new target market already chosen in the assignment 2 will be used to co-create value.
With the information and rationale already presented above, the digital marketing analytics will
be implemented in this market via the integrated approach (Baym 2015). This will lead to higher
rate of converting traffics to real sales and hence a benefit to the organization. Moreover,
because this is a new market target, the digital marketing analytics will offer the best approach
base on RCQ approach.

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References
Baym, N.K., 2015. Personal connections in the digital age. John Wiley & Sons.
Blackwell, C.K., Lauricella, A.R. and Wartella, E., 2014. Factors influencing digital
technology use in early childhood education. Computers & Education, 77, pp.82-90.
Castells, M., 2015. Networks of outrage and hope: Social movements in the Internet age. John
Wiley & Sons.
Cova, B. and Dalli, D 2009. ‘Working Consumers: The Next Step in Marketing Theory?’,
Marketing Theory, vol. 9, no. 3, pp. 315–39.
Grönroos, C. 2011. ‘Value co-creation in service logic: A critical analysis’, Marketing Theory,
vol. 11, no. 3, pp. 279-301.