Conversion Rate Optimisation Lead
London / £50000 - £60000
INFO
£50000 - £60000
LOCATION
London
Permanent
Conversion Rate Optimisation Lead
Digital Marketing Agency
Up to £60,000
Fully Remote Working!
An Award-Winning Digital Marketing Agency are looking for someone to head up their Optimisation function. The role balances strategic and hands-on work and looks to grow the team to 5x profitability!
Company
After finishing an internal restructure to streamline efficiency, this brand are now looking to expand their client's ROI capabilities with an increased emphasis on Analytics & Personalisation being used to drive business decisions. They work across a plethora of sectors, with excellent reviews and a high quality of work being produced.
Role
You would be leading a team of 2 Conversion Rate Optimisation Analysts, and managing 3rd party resources, whilst also owning client relationships and providing technical expertise as-and-when needed. The CRO Lead is expected to evangelise Optimisation internally and externally with clients to sell their team, but also the wider brand.
Skills Needed
- Extensive AB Testing experience
- Ideally, client-facing background but not essential
- Line Management/Leadership experience
Salary and Benefits
- Basic salary of up to £60,000
- Flexible Working
- 5 day working week.
- Private Medical
- 28 days holiday
How to Apply
Please register your interest by sending your CV to Corey Haigney via the apply link on this page.
Key Terms
IBM, Coremetrics, Google Analytics, GA, Omniture, SiteCatalyst, Adobe Analytics, Analyst, Web, Digital, Online, Website, Financial Services, Finance, A/B, Test, Split, Multivariate, MVT, Tracking, Code, Tagging, Tags, Insight, Client, Agency, Management, Strategy, CRO, Conversion, Optimisation, Optimizely, Test and Target, Adobe Target, Maxymiser, VWO, Visual Website Optimiser
CONTACT
Corey Haigney
Recruitment Consultant
SIMILAR
JOB RESULTS
Get Your Data Strategy Ready For The Metaverse | Harnham Recruitment post
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The metaverse has the power to revolutionise how businesses operate, and with the amount of data that will be produced increasingly rapidly – set to reach 160 zettabytes by 2025, businesses will have even more data that will require processing and analysing.
Bloomberg estimates that the metaverse market may grow to $800 billion by 2024.For some, the metaverse offers up a whole world of possibilities for expansion and growth – for others the whole concept is still abstract. Either way, organisations should use this new development as a good excuse ensure best practise is being implemented for their data processes.
As with any emerging technology, the value to be gained will boil down to preparation and having a realistic understanding of what capabilities it will bring to your business. So, what strategies can organisations put in place to maximise their data science capabilities in this new, virtual world?
Preparing for a whole new world
This new blended physical and virtual world will continue to be powered by and generate swathes of data. By 2032, it is estimated that the metaverses alone will have increased our current data usage by 20 times. Accompanying the Metaverse will be a ‘dataverse’ made up of the personal universe of unique data that each person will carry around with them to make their virtual world function.
This will have far reaching implications both for the processing of this data but also around the increased regulation that will be required. As a first port of call, organisations should ensure they have a cloud data platform that can collect and process internal and third-party data with a single point of access, to reduce the time it takes to search for insights.
According to Technative the model must be able to process structured, semi-structured and unstructured data, while also breaking down data silos and unifying insights to learn from the metaverse and help predict future outcomes.
Evaluating current capabilities
Many will be regarding the Metaverse as a huge opportunity to harness real-time data to improve their offering and better inform decision making – whether that be in regard to customer or business insights – and will be assuming only positive consequences for their business strategies and overall competitiveness. 89 per cent of IT and technology industry leaders across the US and UK, consider that data generated by the metaverse will either be very important (55 per cent) or quite important (35 per cent) for their business operations.
AI such as machine learning, will continue to be the key to facilitating these insights – providing efficient and automated ways to interpret real-time data and gain actionable results that can be translated into better informed decisions.Those already adept at using these technologies should not however assume that their current processes will hold up in this new world. The vastness and complexity of the data may require investment in more sophisticated machine learning algorithms and increased data capture processes, not to mention the talent to implement them.
A time to tighten processes
Currently, numerous organisations do not have strategies in place to maximise their data science capabilities, limiting their ability to truly understand the data they have about their company or their customers. The emergence of a new technology provides a unique opportunity to evaluate current data strategies and the usefulness of the data they gather.
Data Science teams should spend this time reflecting on any of the current downfalls they are seeing in their strategy and implementing change based on these lessons so they can effectively prepare for the future. Increased regulation is likely to play a large part in the preparations which will need to be made.
Looking at other recent innovations, such as blockchain and cryptocurrencies, and the issues they faced when it came to regulation can give businesses a good idea around the potential future implications. 60 per cent of all IT and technology leaders planning to integrate business operations within the metaverse are worried about ‘data’ and ‘security’.
These concerns demonstrate the need for unified metaverse standards and practices as well as a code of conduct for collecting different types of data. As a result, we can expect to see regulations being brought in swiftly around the metaverse to ensure businesses are complying and processing data in the appropriate way and are not harming the overall data ecosystem.
An increasing demand for talent
The sheer scale of data sources and siloed data will throw up certain challenges and put a spotlight on those with data analysis and processing skillsets. The regulations that will follow are also likely to further stimulate the data and risk analytics job market.
As automation rises, positions in data analysis are quickly morphing from a data collection exercise into much more business advisory role, which puts more emphasis on the ability of candidates to communicate the implications of the analysis and explain to business leaders how it might impact business decisions.And, as ever, despite increased automation, software and machine-based data processes can only do so much – there will always a certain degree of human intervention required.
The implications on the job market will come several-fold. We are likely to see a vast increase in available roles centred around areas such as compliance and quality, analysis and interpretation, as well as overarching strategic positions that consider the implications of new technology and monitor how processes intersect.
The metaverse provides endless possibilities for business operations but only if the correct infrastructures are in place. Organisations must not rest on their laurels and assume that these large swathes of data will automatically improve their insights and success. As ever, quality results rely on quality data, effective processes and evolving strategies.
Looking for your next big role in shaping the metaverse or need to source exceptional talent? Take a look at our latest Digital Analytics jobs or get in touch with one of our expert consultants to find out more.
What is Product Analytics?
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What is product analytics?
Knowing how well, or not, your customers or service users interact and engage with a product is integral to the success of your business.
Whether it’s a bed from a furniture store or a button on a website, having the insight to understand how easy it is to use or how desirable it is amongst your customer base, then enables teams to go back, tweak the product and optimise it to its full potential.
This is where product analytics comes into its own. Those working within the field – product analysts – are integral in increasing conversion rates – whether that’s purchase rates or how user-friendly a product is – using a mixture of digital customer analytics and data science. From the NHS to Ikea, product analysts are highly sought after in nearly every industry as they strive to make their services and products the best they can possibly be.
What happens if work needs to be done on a product?
Initially, product analysts would undertake testing, such as AB testing, to decipher if there is a more favourable way of presenting the product or service to their customer base. They may also look at implementing tools such as personalisation, a newer capability on the market, to target their service to a specific user, making it more relevant and therefore able to boost conversion.
Once the product analysts have gathered any insights on what would optimise the tools, products, and services, these are then taken to stakeholders to kickstart the process of improvement. From here, updates are made by teams such as those in user experience (UX), and the product is re-launched and continually monitored.
The different arms of product analytics
Product Analytics, while seemingly a straightforward division of Data & Analytics, is extremely broad and split up into a multitude of sub-divisions. So, while all teams may be integral in spotting room for optimisation, their exact role will be different to another analyst.
For example, a trend analyst will analyse trends over a specific period, learning about those patterns and then optimising products or services for those times. Tesco, for instance, will be prepared to put the purchase button of turkey, pigs in blankets, and roasting potatoes at the front and centre of its website at Christmas.
Journey analysts however will measure where customers come from to engage with a product or service, be it a banner ad, an email, or a social media post. They’ll also look at where in the customer journey purchasers or users drop off, finding kinks in the service experience that need to be ironed out.
How to get into product analytics
Like the sound of what a product analyst does? Here’s how to work your way into the industry.
Most businesses will aim to hire individuals with an extremely proficient maths or statistics background; business analytics qualifications will also stand you in good stead as will data science. Additionally, you’ll need to showcase a good understanding of SQL – the tool most frequently used within the sector.
Degrees are no longer as important as they once were, especially in the current climate where there are more vacancies than skilled candidates. Many businesses are far more open to hiring potential employees who hold a few crucial skills and then upskilling them as they go, rather than finding the polished product.
However, the division doesn’t usually see graduate-level talent enter, it can take up to 18 months of work until candidates can think about becoming a product analyst. However, once you’re there you can expect a starting salary of £35,000+ and the opportunities to reach up to £120,000 per year.
Product Analytics is a relatively new division within data and analytics, but one that is gaining traction at rapid rates. By 2028, the area is predicted to be worth $16.69bn as it gains popularity across businesses worldwide, helping them to both streamline and optimise their products and services.
If you are interested in entering the world of product analytics, please speak to one of our team today or take a look at our vacancies here.
Weekly News Digest: August 15th – 19th | Harnham Recruitment post
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This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.
KD NUGGETS: IS THERE A WAY TO BRIDGE THE MLOPS TOOLS GAP?
Interactive notebooks are essential for artificial intelligence (AI) and machine learning (ML) development but are incompatible for production environments. As a result, all ML projects must include a step to covert notebooks into a well-designed software system. This leaves a distinct absence of technology to aid developers in the conversion. So, is productionising notebooks a good idea?Interactive code interpreters are helpful for reporting and exploratory data analysis, but they are not suitable for producing high-quality code for a number of reasons…· There is no test harness· Notebooks discourage modularity· Fault tolerance; if one part of the notebook fails or a computer reboots, data scientists need the ability to pick up work from the last stopping point vs. starting from the beginning· Code review and versioning for notebooks is problematic.We need to reduce workflow bottlenecks and help data science realise data’s full potential with clear separation between the development environment and the production stack.To read more about this, click here.
SOLUTIONS REVIEW: HOW TO DRIVE CONVERSIONS WITH ANALYTICS AND AI TECHNOLOGIES
Before making a purchase decision, consumers often go through a lengthy process that involves researching a product online, reading reviews and opinions, and scouring social media for other people's experiences with the brand. This results in a congested consumer journey for companies trying to attract customers in today's extremely competitive market.To combat these challenges, companies are using marketing as a tool to divert customers' attention at the right stage of the purchasing process by promoting themselves as the superior alternative.The first step toward driving conversions in the marketing pipeline with analytics and AI starts with understanding the typical barriers retailers must overcome with their marketing and outreach strategies. Those barriers include:· Lack of competitor understanding· Increasing demands for data privacy· Missing consumer motivations· Financial cuts for marketing. With the help of analytics and AI, brands can get a clearer picture of typical customer behaviour and trigger points that lead to conversions for their competitors, allowing them to better target their marketing spend to increase sales. To read more about this, click here.
VENTURE BEAT: NINE COMMON DATA GOVERNANCE MISTAKES AND HOW TO AVOID THEM
One of the most crucial components in upgrading or improving the data infrastructure of an organisation isn’t the hardware or software – it’s the data governance that will likely determine the success of the project.A solid data governance programme contains precise rules and guidelines for how data should be produced or obtained, stored, protected, accessed, used, and shared. Both human activity and technological processes are key aspects of the process.To fully understand and manage the data, Data Governance Coach, Nicola Askham shares a guide on the nine biggest mistakes companies make when implementing data governance:· Initiative is IT-led· Not understanding the maturity of the organisation· Data governance is a project· Misalignment with strategy· Not understanding the data landscape· Failure to embed framework· Attempting the big bang approach· Tick-box approach for compliance· Thinking a tool is the answer. Askham closes the report with some advice for companies who are going through the process of implementing data governance. She comments, “Gone are the days when IT made decisions about data because no-one in the business would. Data governance is all about giving that responsibility to business stakeholders and giving them the skills to articulate their data requirements. IT should no longer have to ‘guess’ what the business might want done with their data.”To read more about this, click here.
HARVARD BUSINESS REVIEW: IS DATA SCIENTIST STILL THE SEXIEST JOB OF THE 21ST CENTURY?
Working as a data scientist was the "sexiest job of the 21st century" ten years ago – but is that still the case in 2022?The job is more in demand than ever with employers and recruiters as AI becomes increasingly popular, and the field is anticipated to continue growing at a rate that will surpass most other fields by 2029.In 2019, job listings for data scientists on Indeed had risen by 265 per cent. Now, the median salary for an experienced data scientist in a large city such as California is approaching $200,000.However, the job has changed significantly in the last decade – it has become better institutionalised as the technology used in the field has evolved and the focus on non-technical factors, like data ethics and management, have increased.How the technology operates in companies – and how executives need to think about managing data science efforts – has changed, too, as businesses now need to create and oversee diverse data science teams. Companies need to think about what comes next, and how they can begin to think about democratising data science as it continues to grow.To read more about this, click here. We've loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities.
To learn more about our work in this space, get in touch with us at info@harnham.com.
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