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Author's profile photo Brittany Kilpatrick

A Day in the Life of a Marketing Manager

Meet Kate, a savvy marketing manager who uses SAP Analytics Cloud to optimize her marketing funnel, specifically looking at:

  • Top of funnel traffic
  • Channel and tactic effectiveness
  • Conversion to sale
  • Customer acquisition cost

Kate works for a boutique apparel company that has recently launched a new line of activewear. In addition to overseeing the entire launch from branding to advertising, one of her primary tasks is to create a consistent omni-channel customer experience.

Fortunately, Kate has kept meticulous records of the customer relationship cycle. She collects data from:

  • Organic and paid website traffic
  • Social engagements
  • Online conversions
  • Online revenue generated

By importing all the various datasets into SAP Analytics Cloud, Kate can understand where leads are coming from, the quality of the leads, and the top conversion paths customers take on the site.

Channel Effectiveness

Soon after the launch of the new activewear line, Kate is eager to track her marketing efforts. She uses SAP Analytics Cloud’s business intelligence capabilities to see which campaigns are the most effective at each stage of the buying cycle.

She has five campaigns that she is tracking:

Content marketing from ongoing blog posts and social media promotion

Paid media on three popular social media platforms and one search engine

Affiliate marketing from a dozen popular social media personalities

Email marketing to a list of 2,000 customers

In addition to wanting to see which of the channels produce the highest rate of conversion, she also wants to know the following:

  • Cost per customer from each channel
  • Conversion rate by channel
  • Volume from each channel
  • Average transaction value from each channel

By having the ability to track her marketing efforts visually, Kate uses her customer analytics to predict what could work in the future. Gaining these valuable insights will help her improve the customer experience and increase sales by tweaking her campaigns based on her findings.

Get started today with your free trial for SAP Analytics Cloud. Sign up now. 

Now that Kate has better insight on which channels are most effective, she can reallocate her marketing spend, and A/B test campaigns within the same channel. This will give her better insight as to what calls to action are more effective, what creative resonates with customers, and potential detractors from conversions.

The next metrics she looks at is her customer’s behavior analytics. She tracks customers’ online buying journeys from start to finish and answers questions such as:

  • Which pages, and how many, did they click on before they made a purchase?
  • How much time did a they spent on the site?
  • Did they abandon their cart or follow through with the purchase?

Reducing customer acquisition costs

 

The next area Kate focuses on is Customer Acquisition Cost (CAC) and Customer Lifetime Value (CLTV).

She blends datasets from the following:

  • Purchases
  • Average transaction value
  • Ratio of returns
  • Number of transactions per quarter
  • Referrals (measured through referral bonus system)

Kate is able to understand her customers’ lifestyle preferences and buying habits, which informs her business decisions. She then creates customer profiles and categories to group customers together from most profitable customers to least, as well as group customers by those who share similar behaviors and attributes.

By combining these sources of information, Kate places a CLTV on each customer group and optimizes campaigns that better target each group. These targeted campaigns significantly reduce CAC because they reach the intended audience, which maximizes ROI of the marketing spend. By measuring ROI and forecasting marketing spend, Kate is more informed on allocating budgets for future campaigns.

SAP Analytics Cloud for Marketing Managers

With SAP Analytics Cloud, Kate analyzes her customer data to forecast future behavior and buying habits. This enables her to make more accurate and confident predictions of customer behavior because every decision is based on objective data.

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