The coronavirus pandemic has changed the face of marketing and advertising over the last few months. Marketers are trying to adjust to a new world with changing consumer habits and new strategies that keep brands relevant.
Smartly.io asked 5000 consumers on how social advertising affects the way they see brands.
Their global report ‘How Brands Should Navigate Social Advertising After COVID-19’ offers useful insights into the changes in the advertising landscape and how to address them based on the consumers’ needs.
Here are five changes that shape social advertising during COVID-19:
Content produced in partnership with Smartly.io.
Back in March when uncertainty was kicking off, many brands paused their advertising campaigns. This led to decreased competition on auction-based platforms.
Many industries benefited from this sudden change making the most of the reduced competition and the cost-effective ad results.
The ecommerce industry was encountering supply issues while the travel industry was also dealing with big uncertainty. Thus, while they were trying to adapt to a new reality, they were also seeing lower competition and CPM.
Meanwhile, there were industries that saw their CPM rising due to the rapid change of demand.
Gaming, online education and entertainment advertisers were among those who saw a sudden rise in their CPM.
All of a sudden there was a clear split between different industries and different expectations when it comes to advertising.
As more people increase their social media usage during the pandemic, they also seem to be more willing to engage with social ads. The behavioral change can be attributed to the growing need to connect with others during challenging times.
According to Smartly.io’s survey, users are more open to engaging with advertising on social media, especially in countries that experienced stricter lockdown.
The effect was even more evident in countries like Italy and Spain where the lockdown was stricter pushing users to explore online options for new products and services.
Even in Sweden where the measures were not as extreme as in other countries, 38% of the respondents seemed to be more open to engaging with social ads during the pandemic compared to the past.
It’s no surprise that 59% of consumers started spending more time on social platforms to connect with family and friends.
For brands, this is an opportunity to adopt a human approach that is based on understanding your consumers’ concerns and needs in the current situation.
Not all markets have a clear preference for the most effective ad types.
Still, in countries like France, the preference is clear. 43% of French respondents seem to prefer video ads over images. This doesn’t mean that you should only rely on the consumers’ preferences for the ultimate decision.
The best way to create effective ads is to find a balance between consumer needs and advertising best practices. Testing different formats can be very beneficial when aiming for a successful advertising campaign.
When it comes to messaging apps, US consumers seem to be the least open to receiving them through messaging. Meanwhile, Italians are 4x more open to receiving messaging apps.
We are currently experiencing an unknown situation that affects both consumers and advertisers. Brands need to frame their messaging to be relevant to their target audience.
So what do consumers really want from brands?
According to Smartly.io’s report, one-third of consumers want brands to share relevant messaging that is useful during the pandemic. More consumers expect from brands to continue their advertising compared to the ones who wish they’d stop for the time being.
Moreover, 4 in 10 consumers value ads that promote products and services that are relevant to the current lockdown lifestyle.
Looking at the different markets and how consumers react to brand messaging, there is a correlation between highly digital societies and openness to digital ads.
For example, only 7% of consumers in Singapore think advertising should stop. Meanwhile, less than a third of French consumers want brands to take some sort of action.
There seems to be more cautiousness in the EU and US markets than in Asian in social advertising and how to approach it in the current situation.
Only 32% of American consumers want brands to offer relevant products during the pandemic. Similarly, 21% of German consumers want brands to stop all their advertising efforts for the time being. Indian consumers seem to agree as 23% of them also want brands to stop their advertising during the pandemic.
Thus, global brands can learn useful insights on how to adjust their targeting and messaging for the time being based on the different markets they want to run ads.
Not everything is negative for brands right now. Despite the uncertainty, it’s encouraging to see that consumers are now making more purchases through social media compared to the past.
In fact, consumers in India, Italy and Spain are leading the way with 73%, 71%, and 66% of their respondents making purchases through social ads over the last month.
The lockdown must have affected the increase but it’s still promising to see more consumers being willing to trust brands and new products through social advertising.
The post Five changes in social advertising for brands during COVID-19 appeared first on ClickZ.Reblogged 6 days ago from www.clickz.com
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This past Thursday, Adobe’s Director of Business Strategy Don Bennion gave an enlightening presentation at our online AI in Marketing event.
The talk offered practical advice and examples of how Adobe has increasingly used artificial intelligence and machine learning in their internal activities in recent years.
Their practices are borne from the predictive power of customer experience metrics and their own data driven operating model (DDOM). The company is now moving to make these tools and techniques available to external partners.
Here are my key takeaways.
Attribution was really the first area Adobe started using AI and machine learning.
Moving away from the simple models such as ‘first click’ and ‘last click,’ and later ‘u-shaped’ and ‘linear’ models, the incorporation of AI and ML improved Adobe’s models massively.
Bennion highlights two ways to assign credit in attribution:
Adobe moved into the incremental area. The hypothesis is that if Adobe stops all their marketing efforts, they would still sell products. After all, customers make purchases which are at least partly based on factors like brand loyalty and word of mouth.
Using AI trained on their internal data, they were able to establish a baseline of 50% marketing value from each purchase. From here, they could then weight all their touchpoints individually and – crucially – cut marketing costs, or better allocate it, more efficiently than would be possible by assigning credit with the influential method.
Another area in which Adobe are incorporating AI is in personalization.
‘One to one marketing is the nirvana,’ Bennion says. ‘But segmentation is still an important tool for us.’
In the past, Adobe used to segment from attributes and behaviour. This has value but is clearly limited in the big data era. Adobe’s response was to use AI to develop propensity modelling.
Adobe allocated a ‘propensity score’ for any success event, be it a conversion or a purchase etc. They could then ask the following of potential customers:
…then use this to create segments.
Tech company Nvidia did just this. They created propensity scores for ‘frequent gamers’ and ‘infrequent gamers’ respectively. They understood that both these segments have differing habits when it comes to making purchases, upgrading, or responding to marketing messaging.
The result was that Nvidia could use these predictions to personalize their customer experiences to within 96% accuracy.
One of the key questions that arose during Bennion’s presentation shifted focus to how he has seen data sophistication change over the years.
While attribution and personalization which incorporates AI is still an approximation, he highlighted how the sector is always evolving.
‘More data is accessible from non-web touchpoints. We have better modelling. Speed and processing is quicker with AI data – which is important for drive and scale,’ Bennion said.
That’s not to say there was no mention of any potential bumps in the road for data-driven marketers. Not least in light of Google’s recent announcements to phase out third-party cookies in Chrome within the next 2 years.
Marketers certainly need to be adaptable in this ever-changing landscape, but Bennion is a believer that it is at least as important for them to have a solid understanding of their business, rather than simply striving to better understand data science.
Key growth strategies for Adobe are:
Undoubtedly, growth strategies such as these will be familiar to companies of all sizes, and across many sectors.
For customer acquisition, Adobe is asking: Who has the propensity to buy from us? When it comes to cross-selling, i.e. getting customers to upgrade or make a related purchase, churn propensity models are proving massively useful.
It is clear to see how AI via incremental attribution, as well as propensity modelling which feeds into personalization, is helping Adobe achieve their goals here. And it is not too much of a leap to see how other brands can leverage AI to improve their marketing ROI too.Reblogged 1 week ago from www.clickz.com
It’s not insider information to know the unmatched potential of company-wide AI implementation. Even with all the advancement in recent years, it still feels like we’re only beginning to see what artificial intelligence can do.
There are countless examples of enterprises across dozens of sectors using AI for diverse tasks and processes. Algorithms help firms to predict customer behavior and buying patterns, optimize supply chains, personalize experiences, understand your workforce, and even help you find Waldo.
For some companies, though, implementing and accelerating full-scale implementation is a daunting prospect. Many have concerns over vendors, integration ability, cost, and privacy and regulatory issues. Is the juice even worth the squeeze given these challenges?
So, if you’re thinking of further adopting AI into your processes, or you have begun the transition and are finding it frustrating or tedious, here are five ways to reach your goals quicker.
Source: McKinsey & Company
Like SaaS examples before it, AI is ushering in a new way to do things compared to on-premise software. But with the change, comes challenge. Having C-suite buy-in is crucial for success.
The more informed and engaged the higher-ups are in the uses of AI, the better the chances of enterprise-wide adoption. “Strong executive leadership goes hand-in-hand with stronger AI adoption.
Respondents from firms that have successfully deployed an AI technology at scale tended to rate C-suite support nearly twice as high as those from companies that had not adopted AI technology,” according to this McKinsey Global Institute study.
If there’s no business leader positioned to take the lead of your AI transition, you’re already off to a bad start. Make sure that those in executive positions are tasked with different facets of an AI integration program.
Each step also must be staffed appropriately to drive the process, without being afraid to change the management over the course of a campaign to be successful.
Schedule a weekly teleconference with the key stakeholders to ensure roles are constantly refined, and everyone’s kept in the loop in terms of the adoption status.
It’s also worth stressing that you – as the head of this campaign – need to be able to dictate resources, investment, and overall strategy across the organization. This includes actively engaging those around you for support with AI strategy, human and IT assets, and cultural adoption.
It would help if you made cultural adoption a priority by holding organizational leaders accountable as they execute the revisions needed to continue the transformation. C-suite must remove barriers and obstacles, both technical and cultural, to increase your chances of success.
Once C-suite is aligned with your goals, you need to determine how you wish to manage and control the budget. That’s especially true if your current landscape is made up of competing internal analytics or AI efforts.
Lastly, don’t forget to celebrate and communicate progress to your organization. This helps bolster the commitment from executives as well as gain support for the transformation.
Source: McKinsey & Company
Smarter and more accessible ‘self-service’ and team collaboration software brings with it an increase in data, data sources, and more end-user expectation.
As a result, the demand for proper data governance becomes essential. Without it, the data sits without a purpose in a data lake or warehouse. Look at it this way, more data without restriction can give businesses more freedom.
However, at an enterprise level, it can mean missed steps, inefficient outputs, and oversights. Faster analytics may become a problem before it feels like a solution.
It’s critical to address this with support from executives. This means defined resources to manage and enhance data collection, efficiency, and usage across all vital functions.
The data governance team must, additionally, set out and oversee data policies, standards, definitions, and manage data quality.
Remember, not all data is equal. Define what needs executive control, and which data can be made publicly available for use.
Given today’s availability of more user-friendly analytics and visualization tools, how much ‘self-service’ can be allowed to create better predictive models or different ways of creating new business processes? Who can define these datasets and use cases?
These are vital aspects to consider, as there’s a balance that needs to be struck between being rigid and protective and being flexible. This, again, highlights the importance of a useful data governance model.
Too much control can mean slow processes, lack of response, red-tape, the need for things like email verification, and overt use of business-led IT solutions.
Too much flexibility can mean different versions of the truth, leading to no real ownership or responsibility, conflict, and a reduction in productivity.
As you make decisions about AI, a data governance process allows you to implement and manage said decisions. Including who can access what, how much access, and what that access entails.
All AI adoptions are unique and present their own sets of challenges. And so, you need to begin all AI introductions with a ‘test and refine’ method as opposed to a ‘success or failure’ approach.
Conventionally, analytical methods infer a defined relationship between variables. Trialing a one-sided hypothesis will either validate or reject it, but won’t uncover the hidden connection between the variables; the why.
Creating hypotheticals for each step, and then using these learnings and experiences through the next ones is critical. It means refining and curating your AI deployment until it feels like a workable solution that delivers meaningful results is a much easier process.
And, while this approach will inevitably extend deployment deadlines, it also allows you to fine-tune the outcome to incorporate real-life lessons learned.
If you’re integrating AI into computerised customer service like automated chatbots, it’s vital no matter where the customer goes there’s an answer waiting for them. It can’t work up until a certain point, it needs absolutes. Ultimate solutions will then align with the employee and end-user needs.
Deploying an AI API to ingest a new dataset is straightforward. However, altering the management and training for analysts who’ll be using these processes going forward is a challenge.
Most forms of AI create automated decisions – “yes” or “no.” However, it is often the case that the integration of ML algorithms can allow for more subtle responses as well. These responses may be used in conjunction with existing processes to deliver the best results.
For example, if an AI decision scores say, a loan application on a 1-10 scale of suitability, scores from 7-10 may yield an automatic yes.
However, anything lower will still require human input to grant or deny the application. If you’re integrating AI to analyze voice commands in a call center over VoIP communications, how can it distinguish commands deeper than just “option 1 or option 2”?
Just as you would spend time training employees on how to use a specific process, the same is true for AI-based outcomes.
Human employees may need to spend a few weeks analyzing the results coming back from the AI algorithms. That would give them a frame of reference in terms of how to interpret the scores best.
If you’re using an AI vendor, they can guide in terms of how to understand results and how employees can get the most out of the new system. Otherwise, learning how to create an online learning platform could be a worthwhile investment to get team members up to speed.
AI isn’t ‘magic.’ It’s just a way to understand patterns and behaviors to deliver more accurate results and make predictions. AI only works when it has a defined problem to solve and the right metrics to succeed. If you haven’t clearly defined the issue you’ve bought AI in to solve, you won’t get the right solution.
Source: Harvard Business Review
As you ramp up enterprise-wide AI adoption, what these processes look like in the future will change with the introduction of a multitude of types of automation. From complete manual processes all the way to the adoption of RPA, and even more advanced AI protocols.
It’s best to just (and I know it’s a big just) re-invent business processes from the ground up with AI in mind. You can then apply the best tool for the job at any given step.
Merely inserting RPA or AI into established processes may mean you miss out on all of its potential. You also need to consider the handoffs that need to happen as you further integrate.
This includes human-machine or machine-machine learning. By streamlining the handoffs and making them more seamless and reliable, you can further enhance your future processes to be cost-effective, competitive, and agile.
Source: Harvard Business Review
AI implementation can be sped up. However, it’s not necessarily about being smarter; it’s about making the right choices. Having executive buy-in combined with a defined data governance team is vital.
As is becoming fixated with data quality, dedicating enough time to change management, and having a test without defined expectations approach.
If you’re finding your AI project taking up too much time, be patient. Like any kind of digital transformation, just as you’re approaching the finish line, you’ll likely encounter another hurdle. Overcome it, though, and the possibilities are boundless.
John Allen is the director of global SEO at RingCentral, a global UCaaS, VoIP and video conferencing solutions provider. He has over 14 years of experience and an extensive background in building and optimizing digital marketing programs. He has written for websites such as Hubspot and BambooHR.
The post 5 tips to accelerate your company’s AI implementation appeared first on ClickZ.Reblogged 1 week ago from www.clickz.com
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