META'S NEW SMALL-BUSINESS SERVICE OFFERS GREATER ACCESS TO FIRST-PARTY CUSTOMER DATA--AND IT'S FREE

Since the start of the pandemic, small businesses have increasingly digitized their operations--but it hasn't exactly been the panacea they've hoped for.

Apple's launch of iOS 14.5 in 2021 rolled out an App Tracking Transparency feature, which requires apps to request permission before they can use third-party cookies to track users online. While this was a win for privacy advocates, it was largely seen negatively among businesses that advertise on platforms like Facebook and Instagram, as it curtailed businesses' ability to target potential customers.

Facebook's parent company suggests it has a solution: better access to first-party data. Meta, today, is announcing the launch of a new suite of digital tools through its free Meta Business Suite platform, aimed at helping small businesses better connect with new and existing customers online and convert them. The tools include a quote request option on Instagram, filtering of Instant Forms (onsite paid ads that prompt viewers to input their contact information) that may help businesses to pursue leads that are the best fit for their business, and the ability for businesses to send exclusive content via Messenger to customers who opt in via Instant Forms. Businesses will also be able to download leads from Meta Business Suite into their own CRM system.

"With these new capabilities, you can create a really customized form [through a paid advertisement] to automatically ask potential customers about their needs," says Rich Rao, Meta's VP of small business. For example, a hair salon might ask a customer to choose what kind of service they want when inputting their information through an ad. The goal is to decrease customer drop-off by providing businesses with more information about who is responding to their ads--so those businesses can more efficiently connect with and convert those potential customers. "We think this can fundamentally change the economics for businesses," Rao adds.

Over the past year, reduced access to user data due to iOS 14.5 has made it more challenging and expensive for businesses to target customers through paid social media advertising. This change has been especially hard on small businesses that don't exactly have the big budgets to spend on broadly targeted social advertising. That's according to founders like Hans Schrei and Luis Gramajo, the entrepreneurs behind the Austin-based baked goods company Wunderkeks, who spoke with Inc.com in March. What's more, the proliferation of direct-to-consumer brands on social media has also made it harder for businesses to stand out through paid social advertising, Outdoor Voices and Try Your Best founder Ty Haney also said in March.

Meta's new tools may help small businesses acquire better first-party data, which can help them develop longer, stronger relationships with their customers--but getting customers to notice their ads in the first place will likely remain a challenge.

INFORMATION THEORY: DIGITAL COMMUNICATION

Source of a communication system is Information, can be analog or digital. It’s a mathematical approach to the study of coding of information with storage, quantification, and communication of information.

Conditions of Occurrence of Events

Considering an event, there are three conditions of occurrence.

• If no event, then there is uncertainty condition.
• If event just happened, there is a surprise condition.
• If the event occurred a time back, there is a condition of having some information.

Above three events might occur at different times. The differences in these conditions gives us knowledge on the probabilities of the event occurrence.

Entropy

The possibilities of the occurrence of an event, either surprising or uncertain would be, we are trying to have an idea on the average content of the information from the source of the event.

Entropy is the measure of the average data per source symbol. Claude Shannon, the “father of the Information Theory” provided a formula for it as -

$$H=-\sum _i{p}_i{\log }_b{p}_i$$

Where pi is the probability of the occurrence of character number i from given character stream and b is the algorithm base.

Conditional Entropy is the amount of uncertainty remaining about the input after observing the channel output, is known as Conditional Entropy denoted by H(x∣y).

Mutual Information

Consider a channel whose input is X and output is Y.

Consider the entropy for prior uncertainty be X = H(x)

(This is assumed before the input is applied)

Consider the conditional entropy to know more about the uncertainty of the output after the input is applied. Y = yk

$$H\left(x\mid y_k\right)=\sum _{j=0}^{j-1}p\left(x_j\mid y_k\right){\log }_2\left[\frac{1}{p(x_j\mid y_k)}\right]$$

This is just a random variable for H(X∣y=y0)...............H(X∣y=yk) with probabilities p(y0)............p(yk−1) respectively.

The mean value of H(X∣y=yk) for output alphabet y is −

$H\left(X\mid Y\right)=\sum _{k=0}^{k-1}H\left(X\mid y=y_k\right)p\left(y_k\right)$

= ∑ k = 0 k − 1 ∑ j = 0 j − 1 p ( x j ∣ y k ) p ( y k ) log 2 ⁡ [ 1 p ( x j ∣ y k ) ]

$=\sum _{k=0}^{k-1}\sum _{j=0}^{j-1}p\left(x_j,y_k\right){\log }_2\left[\frac{1}{p\left(x_j\mid y_k\right)}\right]$

Considering the both the uncertainty conditions, i.e., before and after applying the inputs, we got the difference. H(x)−H(x∣y) must represent the uncertainty about the channel input that is resolved by observing the channel output.

This is termed as the Mutual Information of the channel.

Mutual Information is denoted as I(x;y), writing the whole thing in an equation, as below

$$I(x;y)=H\left(x\right)-H(x\mid y)$$

Hence, this is the equational representation of Mutual Information.

Properties of Mutual information

These are the properties of Mutual information.

• Channel has the symmetric Mutual Information.

$$I(x;y)=I(y;x)$$

• It is non-negative.

$$I(x;y)\ge 0$$

• Mutual information can be expressed in terms of entropy of the channel output.

$$I(x;y)=H\left(y\right)-H(y\mid x)$$

Where H(y∣x)is called the conditional entropy.

Joint entropy of the channel input and the channel output is the mutual information of a channel.

$$I(x;y)=H\left(x\right)+H\left(y\right)-H(x,y)$$

Where the joint entropy H(x,y) is defined by

$$H(x,y)=\sum _{j=0}^{j-1}\sum _{k=0}^{k-1}p(x_j,y_k){\log }_2\left(\frac{1}{p\left(x_i,y_k\right)}\right)$$

Channel Capacity

Channel Capacity is the maximum average mutual information, in an instant of a signaling interval, when transmitted by a discrete memory less channel, the probabilities of the rate of maximum reliable transmission of data. Denoted by C and is measured in bits per channel use.

Discrete Memoryless Source

A source where the information is emitted at successive intervals and is independent of previous values is known as discrete memoryless source.

As the time interval is not continuous and discrete, source is discrete. Since the data is fresh at each instant of time, the source is memoryless without taking the previous values into consideration.