Jennifer Layton

 Amazon PPC and Search: Harry’s

Proposal By: Jennifer Layton

December 12^th, 2019 

Introduction: Harry’s launched in 2013 as a direct-to-consumer company with the mission “that was built out of respect for quality craftsmanship, simple design, modern convenience and for guys who know they shouldn’t have to overpay for a great shave.” (Source: Harry’s LinkedIN). In August 2016, Harry’s also became available at retail exclusively at Target stores (Source: Target.com). The brand is also expanding its retail footprint to Walmart (Source: Fastcompany.com).

Audience: Harry’s target audience is millennial men prioritizing convenience and price when shopping for their grooming products.

Business Goals: As a primarily direct-to-consumer company, Harry’s main objective is to sell product. All ROI measurements should latter back to this objective.

Data Collection Recommendation:

• SEMRush: After identifying the likely keywords your customer may use based on product and audience details, use SEMrush which “will provide a list of related terms that you can easily download into a CSV file.” (Source). From there, you will start to understand the most relevant search terms for products.
• Amazon Autofill: Additionally, I recommend starting to enter keywords you have identified into Amazon’s search field to see what begins to auto-populate. This is yet another insight into identifying relevant search terms. (Source).
• Scope: This Amazon SEO research tool “allows sellers to search for relevant search terms based on initial search phrases, as well as search terms for competitor listings”, which is extremely important knowing the competitive nature of the grooming category. (Source).

 (Source)

 Conversion Data:

• Micro Conversion: Clicking on advertisements on Amazon’s website are a measure of a micro conversion (which leads to the overall macro conversion of purchase).
• Macro Conversation: The macro conversion is purchase of Harry’s product(s) on Amazon.com, which directly links back to Harry’s business goal of selling product.

Recommended Amazon Products:

• Enhanced Reporting Insights: Because Harry’s is in a very competitive category, it is important to best understand keywords and placements. “Analyzing performance by placement regularly will ensure that you don’t miss any shifts in key metrics.” (Source)
• Custom Bid Adjustments: As Harry’s is a newer brand and budget’s for advertising spend must be considered. Using custom bid adjustments can manage additional ad placements more effectivity and efficiently, while also ensuring that conversion rate and average CPC align. (Source)

(Source)

Additional Recommendations:

• Ensure all product information (including metadata, product title, images, bullet points and product descriptions) are accurate. Remember there is often incorrect information on Amazon for your product from other counterfeit or third-party sellers. (Source)
• Boost quality of all product listing with Enhanced Brand Content and A+ ages which help brands market their products and lead to stronger conversion rates. (Source)
• Offer more value to consumers via coupons, offers or product bundles. (Source)

(Source)

Conclusion: The Harry’s brand, category and audience where all considered within this proposal. Amazon products consider the fact that Harry’s is in a highly competitive category and must have insight into competitor activity. Additional recommendations consider leveraging the great content Harry’s is already producing on their owned platforms.

Moleskine.com

Web Analytics-Conversions

The Moleskine brand was established in 1997 selling high-quality notebooks. Today, the brand has expanded to sell other paper products and accessories like smart notebooks and apps, bags, books, travel and digital device accessories and writing tools. (Source)

As the brand has expanded their products from solely analog to digital, their web and online social presence has as well. Moleskine.com has presence on both desktop and mobile. The brands primary business goal is to sell products.

Due to the fact that Moleskine products range from notebooks to bags to travel accessories to digital devices, Moleskine.com also has a varying range of target audiences. Overall, we can assume that due to the nature of the products sold, we are targeting creators (whether that be art, business etc.) looking for high-quality products to order online.

Understanding web analytics is extremely important to a company like Moleskine, with a website that primarily focuses on selling products. “The focus is on identifying measures based on your organizational and user goals and using the website data to determine the cusses or failure of those goals” (Shari Thurow). Moleskine.com needs to align their company or organizational goals with the goals of their audience user for the best results.

To do this, Moleskine.com needs to identify both goals. “Setting up Goals in Analytics is one of the most important parts of implementation. Once you enable Goals, you get metrics like the number of conversions and the conversion rate” (Source). As a company and organization, Moleskine prioritizes purchases, upsells and customer loyalty and returns. As a Moleskine.com user, prioritize lie in finding high-quality products available, simply understanding product descriptions and benefits, straightforward purchase process.

Audience members follow the below navigation through the site to the point of conversion, which in this case would be completing purchase.

(Image Source: Shari Thurow Omni Marketing Presentation)

As seen in the above funnel, the path users follow on Moleskine.com leads to purchase. There are also other actions and conversions users could take.

“A macros conversion is typically a completed purchase transition” (Source), as seen in the above purchase funnel. There are also micro conversions which “is a completed activity, such as e-mail signup, that indicates that the user is moving towards a macro conversation.” (Source). When a user comes to Moleskine.com a 10% off coupon pops up upon arrival. This is a micro conversation as it is a completed activity that moves the user towards a macro conversion (purchase).

(Source)

When looking back at the site analytics, there is quantitative information that can start to give insight to the macro and micro conversions of Moleskine.com. For example, bounce rate, daily page views per visitor and daily time on site. Specifically, on bounce rate, Shari Thurow explains that bounce rate can be a good or a bad thing depending on the type of website. In a case where information can be obtained on the first page visited, a high bounce rate could be a good thing (as users quickly and easily found their answer). “If the success of your site depends on users viewing more than one page, then, yes, a high bounce rate is bad.” (Source). As seen above in Moleskines.com’s navigation funnel, users must visit more than one page to complete the macro conversion, purchase.

According to Alexa.com, Moleskine.com’s bounce rate is average at 53.80% but recently decreased by 2%, which is good. We can also see that the daily time spent on Moleskine.com by users has increase 1% to 3:17 minutes. Moleskine.com should look at the variables that could have impacted the lower bounce rate and higher time spent on site, which could potentially be a micro conversion like the 10% pop-up holiday coupon.

(Source)

All the previously analytics that were shared were quantitative metrics, but as Shari Thurow explained, a company needs to understand both the quantitative and qualitative data to best inform improvements and optimization. Follow-up e-mails after a purchase has been completed by a user asking for feedback could give additional insight into what was and was not appealing to users during the site navigation funnel. In this case, Moleskine.com’s conversation data (purchase and using a product coupon) directly link back to their business goals of selling product. Measuring the number of product purchases (and also purchases that used the 10% off coupon) are both indicators of the ROI.

In conclusion, Moleskine.com does a good job of prioritizing their site based on their macro and micro conversions. Immediately upon site arrival a coupon pops-up driving users to the macro conversion of product purchase. Products are also prioritized in the site’s hierarchy listed on the main top navigation, so there is no question on the brand’s business goals. When moving down the navigation tunnel towards product page, the site can get a bit overwhelming. Though having multiple was to find the specific product users are looking for is great, there is a chance users could be overwhelmed by the number of options. Below is an example of three different ways to find a notebook (via collection, limited edition or specialty). There are also limited visuals for the user to view until after moving further down the navigation funnel towards the product page which could be a point for user abandonment.

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Mobile Analytics: Harry’s

Proposal By: Jennifer Layton

 

 Introduction: Harry’s mobile application launched in 2015, about 2 years after the launch of the brand (source). Harry’s mobile and online presence is imperative to its success as a direct-to-consumer brand.

(Source)

Objective:

• Identify the key metrics that Harry’s should be tracking when considering app useage and optimizations.

Platform Considerations: Overall, many analytics platforms have similar tools and metrics that are offered like visits, pages views, geo map overlays and visits by source. Below are unique attributes to some top analytics platforms that can help deliver qualitative data and insights:

• Mobile Download Performance: Harry’s app has a very high rating within the Apple Store with positive comments and few negative comments, which implies a positive download performance. (Source)

• Mobile Conversion Metrics: Taking into considerations Harry’s is a direct-to-consumer brand with an audience of that are heavy mobile users. The Harry’s app focuses on purchases vs. content sharing, in which case the conversion metrics should reflect this object. Install-to-value is an important conversion metric to track. It is also essential to “make sure your app is properly deep-linked” (Source) to understand the impact of promotions and special or season items purchases.
• Key Metrics: Below are the key metric Harry’s should be tracking and optimizing for their app useage:
  • Number of Downloads/Installations: This is a good indicator of the popularity of the app, “but not necessarily its financial success” (Source)
  • Crash Analytics: Knowing details around your app crashes per week will provide insight on how to “understand the technical barriers that are harming your user experience” (Source). This is especially imperative to a direct to consumer online brand (that’s audience are heavy mobile users)
• ROI: Knowing Harry’s is often times purchases as a subscription-based service, tracking the number of visits or visits per week is not ideal. Alternatively it is helpful to understand “ an app’s average revenue per user (ARPU) is the total revenue the app generates within a given time period, divided by the total number of active users for the same period. This gives you the app’s average revenue per user. (Source)

Engagement Increase Recommendation: As mentioned throughout the recommendation, it is important to consider that Harry’s is very often purchased on a subscription basis. Knowing that, the recommendation would be to update content on the site around promotions and special/seasonal products. Harry’s does a lot of partnerships and limited-time offer products backed with social content. These products and content can be highlighted within the app to encourage users to not only engage with content, but purchases items outside of their subscription plan.

 

 

Admittedly, web metrics completely overwhelm me. I work in advertising, and yes, metrics are definitely part of my everyday life, but I like to focus more on the creative and production side of things. That said, Kaushik’s breakdown demystifying web metrics helps me wrap my head around all the numbers and objectives.

According to Kaushik, “metrics are a dime a dozen” (source), so it is important to understand the attributes that a great metric should have. Uncomplexity, relevance, timeliness and instant usefulness are the attributes Kaushik recommends zeroing in on to pinpoint your great metrics. (source),

I have worked on a number of different brands from an advertising perspective, but most being personal care products. For the first time, I am working on a soda brand. Though more and more retail sales are moving out of brick and mortar and to the web, food and beverages still are often purchased in the store.

As I scroll through Mountain Dew’s Instagram page, I can’t help but ask myself, “Is this picture of a bottle on top of a cooler really getting someone to stop by their 7-11 to pick up a bottle of Dew?”. “Does the cute pug in a hoodie holding a Kickstart make you jump in your car to pick one up at Duane Reade?”

My dream metric would be to understand if creative posts on social media directly influenced consumers to purchase a product in store…and why!? I understand there are lots of metrics that can infer that during a time period a social campaign launched there was an uptick in sales. With this information you can infer that the social posts were effective in driving purchase. I would like to understand if a specific post (not a month-long campaign) made a direct impact on purchase and why it influenced a consumer to pick up a Mountain Dew.

My dream metric is a combination of both qualitative and quantitative metrics. First, I would like to understand if a post influenced purchase. This can be explained with quantitive information (increase in sales due to a specific social post). As mentioned in the Forbes article, understanding “the Number of leads generated from each social media channel” would be extremely helpful to understand (source). To expand on my dream metric, I would like to understand why the social post influenced a consumer to purchase, which would be a qualitative metric.

Working at a creative advertising agency, we are continually pushed by our clients to improve and optimize our creative, ultimately to sell more product. This dream metric would not only drill down into how a specific creative concept on social media drove purchase, but also would help explain why. This type of information would take the guess work out of coming up with creative concepts and my team would be able to continually optimize creative to its highest potential.

Data Analysis Proposal

Proposal By: Jennifer Layton

 Introduction: This proposal is comparing and recommending different social analytics tools to be used across GE social platforms

Goals:

• Gain qualitative social data to inform optimizations.

Analytics Tools:

• Hootsuite
  • Create search streams by hashtag, location, or keyword to find compelling and curate content that you can share on the fly. (https://hootsuite.com/platform/content-curation#)
  • See a clear, concise snapshot of your key Facebook, Instagram, and Twitter activity to track trends and measure growth. (See a clear, concise snapshot of your key Facebook, Instagram, and Twitter activity to track trends and measure growth. (https://hootsuite.com/platform/analytics#)
  • Find and filter social conversations by keyword, hashtag, and location—in multiple languages—to hear what people are saying about your brand, competitors, and industry. (https://hootsuite.com/platform/monitoring#)
• Benefit: Ability to track trends and conversations in real time to optimize creative content and participate in trending conversations making your brand more relevant and visible.

 

• SproutSocial
  • Social monitoring & listening tools: Analyze relevant keywords and hashtags to understand brand and category trends and make informed strategic decisions. (https://sproutsocial.com/analytics-and-social-listening/)
  • Identify & Create Brand Advocates: Create tasks and reference contextual information to identify and intelligently engage with leads turning your social media marketing campaigns into tangible ROI. (https://sproutsocial.com/social-media-marketing/)
  • Benefit: Ability to identify brand advocates and influencers to speak about your brand and social marketing campaigns to increase visibility ourside of your audience.

 

• Google Analytics:
  • Proactive Insights: Analytics will automatically surface insights from your data on key changes, new trends and other opportunities you should be aware of. (https://marketingplatform.google.com/about/analytics/features/)
  • Benefit: This tool could be beneficial as social media stories and data to inform trends and content that will resonate with audiences.

Conclusion: After reviewing the different qualitative data tools, I would recommend implementing Google Analytics Consumer Surveys. The primary reason for this recommendation is that the tools available can be used to better understand data about different audiences. Customizable surveys allow for the opportunity to ask questions target toward the different audiences giving insight to areas of optimization.

 

General Electric is a global digital industrial company with products ranging from aircraft engines, power generation, and oil and gas production. The company has presence in approximately 180 countries across the globe. Until recently, the company’s segments included Power, Renewable Energy, Oil & Gas, Aviation, Healthcare, Transportation, Energy Connections & Lighting and Capital. Over the past year, GE has streamlined its focus around its Power and Aviation sectors. (Source)

As mentioned, GE has streamlined its focus and has been in the news a lot lately after sector spinoffs and sales, new CEOs and stock price volatility. Knowing that, understanding GE’s brand sentiment is important. To most “cleanly” pull brand sentiment information on General Electric as a company (vs. productions) I used “@generalelectric”. The mentions that appeared seemed to all do with GE as a company. “@generalelectric” brand sentiment ratings on Social Mention indicated:

• 36%likelihood it is being discussed on social
• 1:1 sentiment (positive compared to negative)
• 31% passion (repeated mentions of brand by same author)
• 31% reach (uniques referencing brand/total mentions)

In 2017, before the more recent company spinoffs and stock price falls, GE launched the #BalancetheEquation campaign. #BalancetheEquation came from an announcement from GE that they established a goal of having 20,000 women filling STEM roles at GE by 2020-which would be a 50:50 representation for all their technical entry-level programs. (Source) The campaign hijacked other trending hashtags to push the message of #Balancetheequation (Image Source). Shandrow states, ” Start by creating and posting more of the kind of content that you notice followers like, share and comment on the most.” (Source). In the case of GE, they almost guaranteed they were using content followers tended to like, share and comment on by using popular and trending content and conversations.

 

After reviewing the campaign that came to life on social, I dug into some of the qualitative and quantitative data to inform how the campaign preformed and how it could have been optimized.

Quantitative

• Ratings of the top languages used when #BalancetheEquation is mentioned helps understand geographically who is participating in the conversations
• Understanding different spelling variants of the hashtag, particularly when it is a main communication point in the campaign, helps optimize content (social or not) to best resonate with the audience

(Image Source)

Qualitative

• Understanding the most used hashtags/phrases that accompany #BalancetheEquation give context to how the audience is talking about the social campaign. This information can be used to continue the conversation with the audience in the same way they are currently having it

(Image Source)

In Shadrow’s article, it states, “The top metrics to track are the number of followers you attract and keep, along with cost per lead and cost per acquisition” (Source). ROI in the case of the GE #BalancetheEquation social campaign would relate to the number of women STEM recruits or hirers they received during the duration of the social campaign to meet their goal in 2020 in comparison to how much paid support they put behind the initiative.

It was a Thursday morning, only a few days after I had started at my new job at Atelier-Leo Burnett. I had worked at two other marketing agencies prior, but this was my first time working at a true advertising agency.

As I made my way to my desk, all I kept hearing people say to each other was, “You qualified! Congratulations!”, “I heard you qualified, how exciting!?”

Qualified? I was stumped…

My co-worker explained that our client had a set of test result scores that a concept had to meet before a commercial script could be produced. This was just the beginning of my experience with concept testing prior to production-and even commercial testing post-production for optimization opportunities. I have seen logos become bigger, end cards become longer, and some concepts have gone to die…all because concepts just didn’t hit the numbers.

A few years later I went to a new agency, and began working on a new brand, Covergirl. At the time, Covergirl was owned by Procter & Gamble, who like many other large consume packaged good companies, put a lot of weight on quantitative testing. That said, Covergirl was treated a little differently. Maybe because they were the only beauty brand in a mostly household-centric consumer packaged goods company, or maybe because Covergirl operated out of a Baltimore office vs. the Cincinnati headquarters. Either way, it worked to my team’s benefit. We were able to fore-go “qualifying” concepts prior to production. There was still importance on data analysis, but contextual, qualitative data worked hand-in-hand with quantitate data.

I am so fortunate that my clients understood the importance of context and feedback because it led to the creation of a piece of work that I am the proudest of.

As Anmol Rajpurohit said, “Almost all business problems have a qualitative aspect, and thus, quantitative analysis alone would never be able to tell the complete story.” We had a list of our “Covergirls” and their followers, stats and Q-Ratings (popularity scores). Instead of slotting in our top-rated celebrity talent, we considered the product benefits and creative concept and took a different direction.

After considering our audience was looking at bloggers and social media influencers just as much (if not more!) as celebrities, we reevaluated our roster for the creative concept we were planning to produce into a commercial. Again, it wasn’t just about looking at which makeup bloggers had the most likes, views and shares, but it was evaluating the context behind their numbers. Why were they trending? Did they have new techniques? What was different about them compared to other established makeup bloggers? What were people saying about them?

“We need to immerse ourselves in the substance that contextualizes big data and allows us to make sense out of it.” (Sean Donahue), and that is exactly what our team did.

This led to the launch of our So Lashy campaign-the project I am probably most proud of throughout my career thus far. We ended up with the perfect cast. We included 2 of our “Covergirls” with wide appeal, Sofia Vergara and Katy Perry. We balanced out our superstars with some up-and-coming young singers and trendy makeup artists with big social followings. Ultimately, this led to Covergirl being the first mass cosmetics brand to feature a male, James Charles, and a hijab wearing woman, Nura Afia, in a national television campaign.

While our team did heavily rely on data analytics like followers, shares and views, this concept would have never come to be without other context behind the numbers. We dug into comments, sentiments and even post content to craft the incredible, diverse So Lashy cast.

“It’s easy to rest on our laurels when the obvious answer might appear to be right in front of us. But disrupting the status quo is just as qualitative as it quantitative — in this case, it’s the only way that we make big data work.” And like Sean Donahue said, we were out on a mission to disrupt the status quo and breakthrough in the cluttered cosmetics market. This would not have been possible without analyzing big data by sifting through quantitative data and surrounding it with contextual qualitative sources.

Citations:

Anmol Rajpurohit; “Qualitative Analytics: Why numbers do not tell the complete story?”

https://www.kdnuggets.com/2014/02/qualitative-analysis-why-numbers-dont-tell-complete-story.html

Sean Donahue; “The Big Data Craze Is Just as Qualitative as It Is Quantitative”

https://www.huffingtonpost.com/sean-donahue/the-big-data-craze_b_5242788.html

 

 

 

 

I don’t consider myself a numbers girl.

All the way back in 2^nd grade, I remember being terrible at “Mad Minutes”. Now at work, I push the creative vision vs. just hitting testing numbers. And living with my CPA husband….I have decided to leave numbers in my everyday life “to the expert”-so I thought…

After reading Margaret J. Wheatley’s article, I took another look at my everyday activities and how I subconsciously let numbers creep in more than I realize. In college, I was obsessed with my GPA. I compare last month’s utility and bills with this month’s. I look at the restaurant bill before throwing down my card and think back to how much I spent last time I was there. Because “you can only manage what you can measure”, right?

Like Wheatley notes, “it is numbers and only numbers that define and make visible what is real”, but numbers can only get us so far without context. When I compare my utility bills I need to consider how many more days were below 60 degrees. When I look at my restaurant bill I need to consider if last time I was there it was happy hour.

For these reasons I rarely compare things I measure with friends and family. Most of the time, context is so different the comparison is just not relevant. Comparing my December heating bill to my brother in Chicago or my property taxes to my best friend in suburban Cincinnati won’t give my any meaningful insights. That said, with the rise of social media, I can seek out others that have more similar living situations or even more similar taste to, in a sense, compare results with.

It often takes time to find relevant situations or context to contract and compare measurements. But productively comparing measurements leads to progress. Whether that is becoming more efficient with your time or money, more knowledgeable in understanding the bigger picture or being able to accomplish goals or tasks- measurements (with contextual relevancy and feedback) can improve our lives…not just consume them!

Citation: Margaret J. Wheatley; “What Do We Measure and Why?”

https://www.margaretwheatley.com/articles/whymeasure.html