of 30 /30
The true meaning of data Data Science meets Marketing Maciej Dabrowski Chief Data Scientist, Altocloud

The true meaning of data by Maciej Dabrowski

Embed Size (px)

Text of The true meaning of data by Maciej Dabrowski

The true meaning of dataData Science meets Marketing

Maciej DabrowskiChief Data Scientist, Altocloud


Real-time analytics Real-time for us is under 1-5s

Q: How many customers are currently on my website?

Q: How many customers are looking at the new article?

Q: How many people from Dublin who spent over 20 minutes on a star wars product page end up spending over 100?


aggregations - slicing a bit like

Predictive AnalyticsQ: Which customers currently on my site are likely to convert?

This talkWhat is Data Science?

Common traps in data analysis

Data Science and Marketing

Data Science

Data ScientistHuman (storytelling) vs. Machine analytics (Machine Learning)

Type A (analytical/statistician) vs. Type B (builder/engineer)

Data ScienceSelect a question and a metricWho is likely to convert? (purchase/conversion rate)

Collect relevant dataUser behaviour (page views) and demographics (device)

Analyse the data and discover patterns10% of returning customers who visit my website on their iPhone after 8pm and spend over 20 minutes end up buying.

Common problemsAm I using correct metrics to answer my question?

What is the quality/accuracy of my data?

Do I use correct visuals and draw the right conclusions?


MetricsCommon metrics:number of sessions/visitsnumber of unique visitorstotal salestime on site

Other metricsconversion rate (percentage)

Is the metric accurate?Monthly visits

Is the metric accurate?Daily visits

MetricsMake sure that you understand how your metric worksHow are the visits counted?

Always challenge the quality of your dataWhat events can influence my metrics?

Use the right metric for the job absolute value vs. percentage

PresentationLabel your axes!

PresentationLabel your axes correctly!

Tricks to make your data look better

Less is moreOverloaded dashboards may hide important facts about data.

Focus on what you want to knowUse charts when you care about trendsUse numbers when you care about absolute valuesUse pie charts when you care about percentages

Simplicity allows you to understand data quicker and easier.

Correlation vs. causation

Correlation vs. causationConclusion: Science is depressing!

Correlation vs. causationConclusion: Cheese makes you more likely to get killed by your bedsheets

Correlation vs. CausationConclusion: Eating margarine will get you divorced!

Data Science for MarketingContent marketingWhich content has the potential to go viral

Marketing successPredict the success of marketing campaigns

Customer analysisPredict churnSegment your customers

Amazon Machine LearningEasy to start

Does not require complex knowledge of Machine Learning techniques and algorithms

Require to move your data to the cloud

Big ML

R ProjectFree desktop tool

Very powerful for advance statistics

Can work with Big Data platforms (Spark)

Requires more knowledge about stats

SummaryMake sure that you understand your data and metrics

Less is more in analytics dashboards

Correlation is not causation

Data science does not require very complex tools!

[email protected]