What Is The Ultimate Outcome Of A Data Warehouse

What Is The Ultimate Outcome Of A Data Warehouse – Many product teams struggle with what to do next. Learn to prioritize daily work through business results. This made our site profitable and increased our traffic to over 5 million monthly users.

For 4 years, I was the general manager of a shopping site that reached 5.5 million monthly active users. This was one research site with 100 million product reviews on 20 million products… think of it as a “shout out to product reviews”.

What Is The Ultimate Outcome Of A Data Warehouse

The main caveat of this article is that all quantitative efforts must be balanced with consistent, quality customer outreach. Over-optimization can mask deeper user experience issues that can be fatal in the long run. If the teams I coach spend as much time with customers as they do in analytics dashboards, they will build better products. However, all product leaders need to think like business owners, and this article turns business into a science.

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Our goal was to help our customers find the right product for them. If they make a purchase we will make a commission.

The KPI pyramid model helped us map the steps from the desired business outcome to our delivered user experience.

(site visitors x click rate x income per click) + (income from ads per impression) = income

We had a dedicated analyst who pulled data from dozens of internal and external systems to calculate our equation each week.

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We were new to managing a site at scale. The main benefit of the KPI pyramid was problem solving.

When a KPI dropped unexpectedly, we quickly checked the section of the site related to that KPI.

Over time, we learned which parts of the site were most relevant to each KPI and filled in the lower levels of the KPI pyramid. We have become optimization experts. (There is a downside to over-optimization… more on that another day).

Problem scenario: The number of visitors will increase but the revenue will remain the same. Not all products lead users to buy. We’ve learned that low-ranking products cause users to go straight back to Google to find something else. So we created a recommendation module to suggest highly rated products in the same category to retain that traffic.

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Problem scenario: Page views will increase but display advertising revenue will remain the same. We learned that advertisers don’t care about every page view on our site. They choose the front page view for any given user. They didn’t want to show any user more than one set of high-value ads in a 24-hour period. And they preferred certain categories of content (electronics, health and beauty) over others (sporting goods, books).

Problem display. Visitors remained steady but revenues declined. From time to time, we launch a site and accidentally or inexplicably remove the monetization module. We did visual QA but no programmatic QA at the time. So these subtle changes show up only in lost revenue due to fewer clicks (lower click-through rate).

Each scenario led to a root cause analysis journey that helped us fill the lower levels of the KPI pyramid. After a while, we had a rich set of advocacy options to resolve any issues that arose.

Also, when you have multiple ways to measure your app or website, you have a backup measurement system in case one of the analysis systems is accidentally removed.

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When we wanted to increase revenue, we looked at the KPI pyramid for areas where we could develop opportunities for each supporting KPI.

Investment scenario: Increase click-through rates before the holiday season. During the holidays, we knew we’d get more traffic on Black Friday and Cyber ​​Monday so we invested in increasing our click-through rate (CTR) to optimize each visit. We did a huge multivariate test and increased our CTR by 70%.

View Investments: Give feedback to our advertisers. Our Display Network advertisers wanted more pageviews to show electronics related ads. So we focused our business development efforts on licensing additional electronics and product review content. This increased our electronics page views (“sending” more visitors for free via Google) and our advertisers filled that inventory with higher value display ads.

Investment Scenario: Increase your income per click. To earn money on purchases, we show our visitors relevant links to hundreds of websites. We trusted these sites to convert a user from a browser to a buyer. We noticed that some sites converted our users better than others. Higher conversions usually lead to higher revenue per click (RPC). So we started emphasizing high RPC sites over low RPC sites. To do this for 20 million products, we need to combine the hand-picked RPC numbers to be automatically included in our website algorithms.

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Investment scenario: promoting website visitors. Our website was built in the age of content. During this time, Google strongly favored sites that created content that matched what users were searching for. So we designed and created shopping research materials based on our review materials. Over time, Google added hundreds of additional ranking criteria that eventually gave more importance to certain forms of content. (More on that another day…)

Although income is so important, it is a lagging indicator. Once you know what your income will be, it’s too late to change it.

The supporting KPIs listed above are also lagging indicators. However, there is one aspect of website traffic patterns that I have been able to use as a leading indicator.

Every leader needs to find reliable leading indicators. They pay attention to what is going to happen. Better in the future.

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He founded Power Reviews, a B2B2C SaaS product review platform, which went public in 2012 at a 13x multiple for $168 million. In the early days of the chain, he managed and architected one of the original e-commerce sites that went public for $66 million in 1999, the online sporting goods retailer Fogdog.com.

For his product teams, he created a curriculum and training program that leverages his 20+ years of experience and the best minds in product management. Additionally, they draw on their software engineering background and experience to bridge the gap between their product and engineering teams. He graduated from Stanford University with a degree in computer science.

Jim is based in San Francisco and works with clients ranging from 2 to 20,000 employees across a variety of industries and business models. Past clients include VSP Global, PagerDuty, Dictionary.com and Hallmark. He has also worked with startups in machine learning, API development, computer vision, payments and digital health.

Previous used “more” engineer time to reduce total engineering time Next Next was lazy and smart. Find and automate your leading indicators. Business insight is a key success factor that affects the performance of decision makers, especially the quality of their decisions. Today, there is a vast amount of data available for organizations to collect and analyze. Data is considered the raw material of the 21st century. Most raw data does not provide much value in its unprocessed state. Extracting knowledge from these datasets can help decision makers gain valuable insights.

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When making a decision, you usually have to consider a large number of options. Each of these must be evaluated using one or more criteria to determine the “best” decision. Mathematical optimization is the discipline of developing realistic plans or schedules to help data-intensive businesses and organizations make better decisions that provide the best balance between customer service and revenue goals.

“Our whole life is about making decisions. So in a way the whole world is about analysis and optimization. Right?” – Jay Menon (IBM Fellow).

According to the Cambridge Advanced Learner’s Dictionary, “optimization” is the process of doing as well or efficiently as possible. Here ‘something’ is a problem or situation to be solved. Mathematical optimization is always concerned with a problem and a set of solutions, described by an abstraction or mathematical model. How ‘good’ is a solution according to a given measure. If the goal is objectively measurable, i.e. money and time, then the closer the solution is to the extremes i.e. minimum or maximum, the better. That is, optimization is a type of search among alternatives.

No one likes to wait in lines to pay for groceries. Checkout cashiers are often the only members of the supermarket staff in contact with customers. For this reason, among others, the checkout operation of a supermarket should always be optimal to give customers a positive impression of the store. This impression can be enhanced by properly timing the cashiers so that customers can be served immediately or spend minimal time in the checkout line.

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Take that into account. You understand that total service is the key to the success of the supermarket. As a scheduling manager, you have to schedule cashiers for a large supermarket with 10 stores in different areas. A total of 100 individual cash registers are planned for 16 hours a day. Typically, you will schedule more than 1000 full-time and part-time cashiers per month. Most are fully trained

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