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Marketing successfully in the digital age relies on data and technology. Now, the data collected by technologies are used by more advanced technologies in order to automate and optimize every step of the marketing lifecycle. Marketing optimization turns out to be a fundamental need of any company.
Data science provides marketers access to useful data collected from a variety of channels like website SEOs, email marketing, social media. Usage of Data Science in marketing optimization turned out to be more fruitful since more meaningful and accurate insights could be derived from bulk dataset within a short time.
Marketing optimization is the process of improving the marketing efforts of an organization in an effort to maximize the desired business outcomes. Marketing optimization can be applied to individual marketing tactics employed or as a whole into your marketing campaign.
Steps In Marketing Optimization Using Data Science
There are mainly three steps in marketing optimization, namely:
1) Collect and Analyze Marketing Data:
Every marketing optimization tactic begins with the accumulation of useful data. Key target data to track may include visitor Browser and Device, visitor behavior and browsing path, referring URL, etc.
Analyzing the data, you may see that some campaigns cost too much, and yet develop little leads and vice versa. Once, you understand the impact of your current campaign, you can strive towards modifying it, from the ground up, if necessary.
2) Refine the marketing optimization process:
Refining your marketing optimization process should result in streamlined goals, communications, and responsibilities. Investing more money in an ad campaign doesn’t always bring in more customers. Budget allocation should be rightly done in order to nurture marketing tactics that actually bring you customer footfall and to take it to the next level. Data science can provide us with models to calculate and optimize Return On Investment(ROI) while implementing a marketing tactic.
3) Find opportunities for automation:
Automation increases reliability, responsiveness and provides you time and space for further development. Rather than directing an ad campaign for the general public, with enough data, you can actually find your target audience among the masses and direct more resources to leads with higher success rates.
Now, let’s dive into the business model of some famous startups and see how they used data analytics on marketing optimization.
Case Study of Airbnb That Uses Data Science
Almost everyone has heard of Airbnb. It is a unicorn startup founded in 2008, which offered an accommodation booking engine. It’s a place where people are looking for short-term accommodations. So what role do data science and analysis play in a company with a turnover of over $1 Billion in a single quarter?
Data tackling diversity:
Airbnb uses data to not only improve their services and search but their hiring practice and customer groups as well. They’ve actively looked to hire female data scientists to the team, and they checked at the top of their hiring funnel that about 30% of the applicants were women.
They took upon the mission to increase the odds and arranged several community events and talks. They also made sure that, in the interviewing process, applicants were a match analytically, communicatively, and culturally.
Improving search using data:
The heart of Airbnb’s business model is its search. Originally, Airbnb struggled with providing useful data to the customer and settled for a model which returned the highest quality listings within a certain radius from the user’s search. As more data began to flow in, and the business was scaling, they replaced its basic search with a more data-driven one.
They prepared a rich dataset consisting of a host and guest interactions and built a model that estimated a conditional probability of booking in a location, given where the person searched. This means that searching a place would also show results of neighborhoods where users with similar searches booked in. The team also replaced the “neighborhood” links, with top traveling destinations in some countries and saw a 10% lift in conversions from users in those countries.
Using data to determine host preferences:
Data scientists in the team found out that, in big markets, hosts preferred no calendar gaps between bookings so as to maximize their profits whereas, in the small markets, hosts preferred some calendar gaps in their bookings. This was converted into a full-scale machine learning algorithm that looked at everything from the host’s prior acceptance and decline decisions to the particulars of the current trip.
They applied filers to clear out their noise and finally tested it using probability and ranking algorithms that took other preferences into consideration. This endeavor saw a 4% increase in booking conversion as well as a significant increase in the successful matching of guests and hosts.
Case Study of Netflix That Uses Data Science
Netflix started as a DVD rental service in 1998. It introduced an online streaming service in 2007. For this to be a reality, Netflix invested in a lot of algorithms such as the recommendation system to provide suggestions to the user. It collects data from each of its users and with the help of data science, understands the behavior and watching patterns. It then leverages that information to recommend movies and TV shows customized as per the user’s choice and preferences.
Netflix collects information of a user such as:
- The device used to watch the show.
- If the user pauses the show, do they resume watching?
- Time and date when a user watched a show.
- Does the user binge-watch an entire season of a TV show?
- If they do, how much time does it take to binge-watch it?
Such information combined with the ratings that the viewer gives to particular content, the number of searches, is used to make a detailed profile of the user. It then leverages data analytics to make a robust recommendation algorithm that suggests the best content according to the user profile. As per a study, the recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers and has helped Netflix earn one billion customer retention.
Case Study of Doordash That Uses Data Science
DoorDash is a triple-sided marketplace and logistics platform that enables a customer to order food on-demand from restaurant merchants that are then delivered by “Dashers”, a fleet of delivery people. The food delivery space is a very competitive space with big fishes such as UberEats, GrubHub, etc. DoorDash has implemented data science algorithms in selecting the right restaurants for customers, minimizing delivery times, and making sure that the supply of Dashers meets the demand.
DoorDash uses machine learning to present a personalized selection of recommended restaurants for users based on their past search and order history. By showing users restaurants they are likely to order from, DoorDash has lifted its conversion rate from search to checkout by 25%.
Preventing fraudulent exploitation of the app:
Doordash had to take measures against fraudsters using stolen credit cards and reselling DoorDash as a service illegally. Earlier, no automation was in place and most fraud prevention was done via manual review. To overcome this problem, the DoorDash team used Sift’s Payment Protection product to ensure the integrity of their community.
Using the network view, the team can see how many users on the platform are connected, enabling quick identification and removal of colluding fraudsters or fraudsters using multiple accounts.
Using Workflows, the team automated the labeling of fraudulent-looking users with the help of a machine learning model that can recognize fraudulent behavior.
With Sift, they’ve increased their efficiency by 200-300%.
In this article, we have seen the significance of data science in marketing optimization and we have discussed the advantage data analytics provide over the competitors. We have also seen case studies of famous unicorn startups such as Airbnb, Netflix, and DoorDash and seen how they have used data science to build their business model.