• To combat this, Alavi’s artificial intelligence (AI) engine identified products to market and audiences to target, which the company used to boost revenues without sacrificing its bottom line.

Recognising the Internet’s Potential

Starting life as a small nursery in 1965, Far East Flora Holdings is today, one of Southeast Asia’s leading florists. While the company has established a network of brick and mortar stores in Singapore, Hong Kong and Malaysia, it was quick to recognise the potential of the internet as a retail channel and launched FarEastFlora.com (FEF) as early as 2000. Today, the e-commerce site, led by its Managing Director, Ryan Chioh, offers over 1,000 flower and gift options and delivers to more than 140 countries.

Profitable Growth Now

In recent years, to market its innovative products online, FEF has relied on pay-per-click (PPC) advertising through platforms like Google and Facebook. Though year-on-year traffic growth has been good, conversions and revenue have not met the company’s expectations. This recently became an especially serious issue because FEF was forced to raise its ad spends to counter increased competition as well as target broader audiences (who generally have a lower intent to buy) to grow its customer base. The company knew the situation was unsustainable and improvements had to be made.

What FEF urgently needed was to boost its return on ad spend so it could achieve profitable growth. MD Ryan firmly believed the key to this was data. While he knew his web analytics platform collected vast amounts of information on customers’ purchase journeys, he didn’t have a cost-effective way to analyse all of it. Overcoming this issue was imperative to identifying potentially profitable audiences and extracting insights that would make FEF’s digital marketing more competitive, effective and efficient.

The solution Ryan found was Alavi.ai (Alavi), an online application that uses artificial intelligence and data science to analyse marketing data. He was keen to give Alavi a go because of its proven record of helping small and medium businesses grow profitably by greatly improving their PPC advertising. Quick Setup. Quick Results.

For FEF’s Marketing Communications Manager, Chris Kok, linking Alavi to the company’s ad platforms and customer relationship management (CRM) data (which Alavi encrypted) was straightforward and took very little time. Once connected, Alavi’s automated AI engine quickly kicked into gear and identified products that were most likely to sell in the coming weeks, as well as cohorts to target for immediate returns and profitable growth. By providing a marketing workflow along with cohort attributes that were compatible with Google and Facebook, FEF was able to immediately launch campaigns on its preferred ad platforms.

In just 3 weeks, with Alavi’s AI, FEF improved its online marketing considerably. Return on ad spend (RoAS) was increased by 21.75%, while average order value (AOV) shot up by 20.33%.

Future Success

When it comes to flowers and gifts, trends and tastes are constantly changing. To help FEF keep on top of what’s new, Alavi constantly tracks user behaviour, so the company has the insights it needs to keep innovating and keep marketing the right products to the right people. Alavi’s ability to perpetually identify new opportunities from the top to the bottom of the customer funnel will ensure FEF continues to accelerate its growth profitably well into the future.

• Alavi used artificial intelligence (AI) to identify profitable audiences, marketing workflows and target 2x revenue growth.

An Indian Pioneer

Macmerise is a rising star in India’s tech gadgets and accessories industry. It is best known as India’s first decal manufacturer and the first company in the country to be awarded licensing rights by major Hollywood studios including Disney, Marvel, Lucas Films and Warner Bros (DC Comics).

Like most online retailers, Macmerise invested significantly in pay-per-click (PPC) marketing to grow its business. While initial results were good, increased competition eventually resulted in soaring acquisition costs, diminishing returns and, worst of all, stagnant revenue growth.

The Need for an Advantage

To optimize its PPC marketing, Macmerise relied heavily on the targeting and conversion features offered by online ad platforms, like Facebook and Google These are tools every advertiser has access to, which meant Macmerise and its competitors were using similar strategies. Macmerise CEO, Sahil Shah realised that the company’s digital marketing did not have any competitive advantage.

To differentiate its strategies, Sahil and the company’s digital agency, Omnikon, searched for new marketing technologies. They wanted an application that would deliver a deeper understanding of how customers were engaging with their products and content and then identify new audiences that have a high probability of buying from Macmerise versus the competition. After an exhaustive search, they discovered Alavi.ai (Alavi), an online application whose artificial intelligence has helped brands across a broad range of industries greatly improve their digital marketing.

The Power of Artificial Intelligence

For Macmerise, setting up Alavi was simple and intuitive. In just a few of hours, Alavi was connected to all of the company’s ad platform analytics and automatically started searching for the company’s most profitable audiences. To do this, it used artificial intelligence to analyse the relationships between millions of data points. It then reverse engineered profitable conversion paths based on key metrics including cost per acquisition (CPA), return on ad spend (RoAS) and conversion rates.

Using Alavi’s cohort profiles and recommended workflows, Macmerise quickly launched a series of highly targeted campaigns via online platforms like Facebook and Google. The company also personalised each ad campaign’s creatives using the interests, affinities and demographic variables identified by Alavi.

Immediate Results

In just four weeks, Alavi-powered campaigns beat all of Macmerise’s previous benchmarks. Cost per Acquisition reduced by 42%, Conversion Rates increased by 42% and Return on Ad Spend improved by 25%.

Buoyed by this success, up to 70% of Macmerise’s campaigns are now driven by Alavi, and the company is on track to doubling its revenue.

• Using Alavi’s data science capabilities, J. was able to identify and focus its marketing spends on audiences that had high probability of converting, which boosted it returns and growth.

The Battle Is Online

Established in 2002 with the aim of reviving South Asia’s rich textile heritage, the apparel retailer J. is today one of the region’s most successful brands. Catering to both women and men, the company operates over 100 local stores as well as another 20+ internationally in the UK, Australia, Canada, New Zealand, the UAE and Qatar.

With the internet revolutionizing shopping all over the world, J. launched its own e-commerce store. However, in recent years, with the number of brands selling online increasing almost exponentially, getting consistent growth has proved to be very difficult. This is because the competition on pay-per-click (PPC) advertising platforms (like Google and Facebook) has been especially fierce with marketing returns declining and budgets skyrocketing.

A New Weapon

To fight rising marketing costs and reignite growth, J. knew it needed to optimize its PPC advertising and set itself two main goals: reduce cost per acquisition (CPA) for existing customers and acquire new ones with a controlled return on ad spend (RoAS).

To do this, it turned to data science, a tool that had helped many companies around the world solve similar problems. However, the cost of setting up its own data science team was prohibitively expensive. Fortunately, there were new marketing technologies that could help. After weighing the pros and cons of many of them, the company chose the advanced online targeting application, Alavi..

Offering both artificial intelligence (AI) and machine learning capabilities, Alavi was not only affordable but could quickly and easily integrate with the brand’s existing infrastructure and ad platforms. Upon connecting Alavi, its AI engine analysed a variety of variables from its marketing and customer data to identify audiences that were highly profitable and worth investing in. The insights Alavi provided helped the company target desirable cohorts with pinpoint accuracy and eliminate ones that had very little chance of converting. It also assisted the marketing team in developing ads that would improve engagement with the audiences it recommended so the company would get better returns.

Immediate Success

Working with Alavi’s customer success team, the apparel brand launched a series of campaigns that were closely monitored to test the application’s effectiveness. In just 4 weeks, the Alavi.ai-assisted campaigns delivered results that not only exceeded expectations but broke existing standards: CPA was reduced by 77%, average order value grew by 50% and RoAS increased by 300%.

Buoyed by their success, J. is now gearing up to test Alavi.ai’s other advanced features, such as data-driven attribution modelling and engagement optimization. The battle for online success in the apparel industry requires strong allies, especially ones you know you can count on.

• Taking advantage of Alavi’s data science capabilities, Sapphire optimised its digital marketing campaigns which not only stabilised spends but also increased returns.

Intense Competition

Launched by one of the biggest names in Pakistan textiles, Sapphire is a celebrated fashion brand known for exquisite designs at affordable prices. Built on an uncompromising commitment to quality, the company offers everything from clothes and shoes to bags and linen through its e-commerce site.

But with online shopping booming in Pakistan and barriers to entry relatively low, new companies are entering the fashion space all the time. There are already over 60 brands selling clothes online who get between 20,000 and several million site visits per month. Most of them are fighting to attract, engage, convert and retain the same audience segments.

This rapid increase in competition was a major challenge for Sapphire because it raised the company’s pay-per-click (PPC) marketing costs substantially on multiple ad platforms including Google and Facebook. Sapphire needed to find a solution that would help it both in the short term and the long term.

Getting Ahead with Data Science

By studying what was happening internationally, Sapphire’s management team fully understood the potential of data science and how it could substantially improve online advertising returns as well as deliver consistent outcomes. After weighing the pros and cons of investing in their own data science team, they explored marketing technology options with similar capabilities.

One ‘martech’ that caught their eye was the precision targeting application, Alavi. It performed predictive analytics using customer user behaviour, which would allow Sapphire’s digital marketers to quickly and easily target their most profitable audiences. This would help maximise the efficiency and effectiveness of their campaigns. Also, because it was developed for small and medium businesses, Alavi was affordable and had the potential to deliver results almost immediately.

Exceeding Expectations

An online application, connecting Alavi to Sapphire’s web analytics and marketing platforms (Google, Facebook etc.) was simple and fast. Once linked, Alavi’s artificial intelligence (AI) and machine learning capabilities started working right away. Using data science to analyse data generated from past campaigns, Alavi was not only able to identify the best audiences for Sapphire’s current efforts but also audiences for expanding and remarketing.

In just one quarter, on the campaigns Sapphire used Alavi, digital marketing spends were finally optimized. This resulted in bigger earnings from existing customers as well as the acquisition of many new ones. Thanks to Alavi’s data science capabilities, Sapphire beat its return on ad spend (RoAS) target by as much as 35% and increased its average order value (AOV) by 16%.