Forecasting Weekly Sales at Amanta Case Study 1. Read the attached case intro and review the accompanying data. 2. Analyze the data and build a forecast fo

Forecasting Weekly Sales at Amanta Case Study 1. Read the attached case intro and review the accompanying data. 2. Analyze the data and build a forecast for the remainder of 2019 and the next year, 2020. 3. Write a report to management to provide your forecast and be sure to address important issues related to how you developed the forecast and your confidence in using it. I’ve included an article from Harvard Business Review that was part of our weekly materials. Forecasting Case
The accompanying data shows the number of units sold of Product A each quarter
beginning in 2011 for ABC Company. You have been asked to analyze the data
and build an appropriate forecast for the remainder of 2019 and next year, 2020.
Forecasting Case
The accompanying data shows the number of units sold of Product A each quarter
beginning in 2011 for ABC Company. You have been asked to analyze the data
and build an appropriate forecast for the remainder of 2019 and next year, 2020.
MARKETING
Story-driven Data Analysis
by Judy Bayer and Marie Taillard
SEPTEMBER 27, 2013
Great analysts tell great stories based on the results of their analyses. Stories, after all, make
results user-friendly, more conducive to decision-making, and more persuasive.
But that is not the only reason to use stories. Time and time again in our experience, stories
have been more than an afterthought; they have actually enabled a more rigorous analysis
of data in the first place. Stories allow the analysts to construct a set of hypotheses and
provide a map for investigating the data.
We recently worked with a department store retailer and a team of analysts looking for
creative insights into customer loyalty. Based on our work with a department store expert,
we started out with a storyline, a narrative hypothesis, according to which a customer
experiences different journeys through the department store over time and rewards the
retailer with a certain level of loyalty.
How will these journeys unfold? Does the customer start in cosmetics and then move into
clothing? Does she go from the second floor to the first floor to buy a handbag to match a
new outfit? Does she have shopping days where she takes a lunch break in the restaurant
before continuing her shopping? Do less loyal customers make different journeys from
more loyal ones?
In other words, we were interested not just in what customers were buying, but in the
mechanics of how they make their purchases and how this may make them loyal. After the
analysis, the true story of a customer’s path to loyalty is in fact revealed.
Where do these stories come from? In our experience, they can come either from the
experience of an expert in the sector or brand, as was the case in the previous example, or
from qualitative research using observation or in-depth interviews.
We recently advised a telco client in developing the “jobs to be done” for a range of new
products and services. We interviewed consumers and heard their own stories of how they
go about using their mobile devices throughout the day. The general narrative hypothesis
we drew from listening to these stories is that consumers cobble together mobile solutions
to suit their lifestyles.
One consumer revealed that he actually owns two SIM cards for the same smartphone and
told us in what context he changes from one to the other. Another customer told us about
the parental control and other relevant apps and browsing that she has discovered and
collected and which facilitate her lifestyle as a mother.
What we are seeing here is a multi-usage context (characterized by two SIM cards) and a
“Mobile Mommy” context, each of which calls for a distinct analysis and possibly different
products/services to be developed subsequently. In other words, we found that the
customers’ homemade solutions could be used by brand managers to identify what kind of
data to gather and what kind of analysis to perform.
The analyses will in their turn enrich the initial stories and lead to deeper insights. What is
important here is that the storyline, told before the analyses, enables an authentic human
element to surface that would be more difficult to extract from the data alone
In order for a story to truly enable analytics, the story development process needs to be
rigorous. We use the framework of Grounded Theory to ensure that the data and
overarching storyline inform each other and are coherent with each other. The idea is for
the analyst to navigate back and forth between the data and the developing story to ensure a
good balance between the creative narrative and the analytics that reveal the facts and
details of the story.
The enabling storyline should not be too restrictive: it needs to support the development of
the plot and characters as they emerge from the analysis, but without bias. Conversely, the
storyline can suggest specific questions to be asked of the data for a more in-depth analysis.
In a world that’s flooded with data it becomes harder to use the data; there’s too much of it
to make sense of unless you come to the data with an insight or hypothesis to test. Building
stories provides a good framework in which to do that.

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